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CN112513663B - Motion detection for passive indoor positioning system - Google Patents

Motion detection for passive indoor positioning system Download PDF

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Publication number
CN112513663B
CN112513663B CN201980051749.XA CN201980051749A CN112513663B CN 112513663 B CN112513663 B CN 112513663B CN 201980051749 A CN201980051749 A CN 201980051749A CN 112513663 B CN112513663 B CN 112513663B
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Prior art keywords
computing device
user computing
wireless signal
access point
location
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CN201980051749.XA
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CN112513663A (en
Inventor
陈辉芳
阿毕实·穆赫吉
彭荣
奥斯卡·贝加拉诺·查韦斯
桑托什·甘诗雅姆·潘迪
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Cisco Technology Inc
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Cisco Technology Inc
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Priority claimed from US16/103,781 external-priority patent/US10349216B1/en
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    • GPHYSICS
    • G01MEASURING; TESTING
    • G01SRADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/14Determining absolute distances from a plurality of spaced points of known location
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01SRADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/0009Transmission of position information to remote stations
    • G01S5/0018Transmission from mobile station to base station

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  • Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

The enterprise system configures an access point device at the enterprise location to communicate with the location determination system (140). The location determination system receives wireless signal attributes of a user computing device (110) broadcasting Wi-Fi signal data at an enterprise location from one or more access point devices (130). For a particular time window, the location determination system determines aggregate characteristics of the received wireless signal data across all access point devices and classifies each user computing device as mobile or stationary by applying the wireless signal data to a model. For each user computing device determined to be mobile, the location determination system calculates a respective location of the user computing device based on the wireless signal data. For each user computing device determined to be stationary, the location determination system does not calculate a respective location of the respective user computing device.

Description

Motion detection for passive indoor positioning system
Technical Field
The present disclosure relates generally to indoor positioning systems, and more particularly to determining a user computing device location based on wireless signal data of the user computing device.
Background
Large-scale positioning systems that track thousands of devices and utilize hundreds of access points ("APs") have become an extremely important component of real-world wireless deployments. Customer participation, asset tracking, and indoor navigation are some specific applications that may utilize indoor positioning systems. These positioning systems may use angle-of-arrival ("AoA") and/or received signal strength indication ("RSSI") measurements to achieve position accuracy within one to three meters, each position calculation is typically expensive. Inertial sensor data traditionally used to detect movement of a user computing device is not generally available to a position determination system. Since users are often stationary in an enterprise facility (workplace, hospital, etc.) for a considerable amount of time, the positioning system unnecessarily recalculates the position calculation. Thus, existing indoor positioning systems have provided considerable accuracy, but limited scalability and coverage.
Current applications for determining the location of a user computing device do not provide for efficient positioning based on motion classification.
Disclosure of Invention
The technology herein provides a computer-implemented method of determining a location of a user computing device based on wireless signal data and motion classification received at an access point. In an example, an enterprise system configures an access point device at an enterprise location to communicate with a location determination system via a network and receive Wi-Fi signal data via a wireless communication channel. One or more users configure respective user computing devices to broadcast Wi-Fi signal data at an enterprise location. The location determination system receives wireless signal attributes of a user computing device broadcasting Wi-Fi signal data at an enterprise location from one or more access point devices via a network. For a particular time window, the location determination system receives wireless signal attributes of the user computing devices over the time window from one or more access point devices, aggregates the received wireless signal attributes, and determines whether each user computing device is mobile or stationary by applying the aggregated wireless signal attributes to the model. For each user computing device determined to be mobile, the location determination system calculates a respective location of the user computing device based on wireless signal data associated with the respective user computing device and displays the determined location via a user interface. For each user computing device determined to be stationary, the location determination system does not calculate a respective location of the respective user computing device. The location determination continues to receive wireless signal attributes from one or more computing devices at the enterprise location that continue to broadcast Wi-Fi signal data via the network and continues to determine a location of the user computing device based on the motion classification.
In certain other example aspects described herein, systems and computer program products are provided for determining a location of a user computing device based on wireless signal data and a motion classification received at an access point.
These and other aspects, objects, features and advantages of the illustrative embodiments will become apparent to those of ordinary skill in the art upon consideration of the following detailed description of the illustrative embodiments.
Drawings
Fig. 1 is a diagram depicting an example system for determining a location of a user computing device based on wireless signal data and motion classification received at an access point, in accordance with certain examples.
Fig. 2 is a block flow diagram depicting a method for determining a location of a user computing device according to a motion classification based on wireless signal data received at an access point device, in accordance with certain examples.
FIG. 3 is a block flow diagram depicting a method for registering an account with a location determination system and downloading an application to a user computing device by a user, in accordance with certain examples.
Fig. 4 is a block flow diagram depicting a method for receiving wireless signal data of a user computing device from an access point device by a position determination system, in accordance with certain examples.
Fig. 5 is a block flow diagram depicting a method for determining, by a location determination system, a motion classification of a user computing device based on wireless signal properties of the user computing device over a time window, in accordance with certain examples.
Fig. 6 is a diagram illustrating an example configuration of an example user computing device and a plurality of access point devices at an example enterprise location at time T, according to some examples.
Fig. 7 is a diagram illustrating an example method for extracting features from Wi-Fi signal data received at multiple access points, according to some examples.
FIG. 8 is a block diagram depicting computing machines and modules according to certain examples.
Detailed Description
Overview
Example embodiments described herein provide computer-implemented techniques to determine a location of a user computing device based on wireless signal data and motion classification received at an access point.
In an example, an enterprise system configures an access point device at an enterprise location to communicate with a location determination system via a network and receive Wi-Fi signal data via a wireless communication channel. One or more users configure respective user computing devices to broadcast Wi-Fi signal data at an enterprise location. The location determination system receives wireless signal attributes of a user computing device broadcasting Wi-Fi signal data at an enterprise location from one or more access point devices via a network. For a particular time window, the location determination system receives wireless signal attributes of the user computing devices over the time window from one or more access point devices, aggregates the received wireless signal attributes, and determines whether each user computing device is mobile or stationary by applying the aggregated wireless signal attributes to the model. For each user computing device determined to be mobile, the location determination system calculates a respective location of the user computing device based on wireless signal data associated with the respective user computing device and displays the determined location via a user interface. For each user computing device determined to be stationary, the location determination system does not calculate a respective location of the respective user computing device. The location determination continues to receive wireless signal attributes from one or more computing devices at the enterprise location that continue to broadcast Wi-Fi signal data via the network and continues to determine a location of the user computing device based on the motion classification.
Using and in dependence upon the methods and systems described herein, the network system may detect motion of the user computing device by comparing phase vectors and RSSI data determined from Wi-Fi signals of the user computing device received at one or more access points. The systems and methods herein provide new motion detection models that exploit the changes in measurement results and the spatio-temporal relationship with respect to device motion and achieve considerable accuracy while incurring negligible computational overhead. In this way, the systems and methods herein provide computational savings with negligible impact on position accuracy.
Various examples will be explained in more detail in the following description, which is read in connection with the accompanying drawings that illustrate the program flow.
Turning now to the drawings, wherein like numerals denote like (but not necessarily identical) elements throughout the several views, exemplary embodiments are described in detail.
System architecture
Fig. 1 is a block diagram depicting a system 100 for determining a location of a user computing device 110 based on wireless signal data and motion classification received at an access point, in accordance with certain examples. As shown in fig. 1, system 100 includes network computing devices 110, 130, 140, and 150 configured to communicate with each other via one or more networks 120. In some embodiments, a user associated with a device must install an application and/or make feature selections to obtain the benefits of the techniques described herein.
In an example, network 120 includes a wired or wireless telecommunication mechanism by which network systems (including systems 110, 130, and 140) can communicate and exchange data. For example, each network 120 may include, be implemented as, or may be part of: storage Area Network (SAN), personal Area Network (PAN), metropolitan Area Network (MAN), local Area Network (LAN), wide Area Network (WAN), wireless Local Area Network (WLAN), virtual Private Network (VPN), intranet, internet, mobile phone network, card network, bluetooth Low Energy (BLE), near field communication Network (NFC), any form of standardized radio frequency, infrared, sound (e.g., audible sound, melody, and ultrasound), other short range communication channels, or any combination thereof, or any other suitable architecture or system that facilitates communication of signals, data, and/or messages (commonly referred to as data). Throughout this specification, it should be appreciated that the terms "data" and "information" are used interchangeably herein to refer to text, images, audio, video, or any other form of information that may exist in a computer-based environment.
In an example, each network system (including systems 110, 130, and 140) includes a device having a communication module that is capable of transmitting and receiving data over network 120. For example, each network system (including systems 110, 130, and 140) may include: a server, a personal computer, a mobile device (e.g., a notebook computer, a handheld computer, a tablet computer, a netbook computer, a Personal Digital Assistant (PDA), a video game device, a GPS locator device, a cellular telephone, a smart phone, or other mobile device), a television having one or more processors embedded therein and/or coupled thereto, an appliance having one or more processors embedded therein and/or coupled thereto, or other suitable technology including or coupled to a web browser or other application for communicating via network 120. In the example shown in fig. 1, the network systems (including systems 110, 130, and 140) are operated by user 101, access point device 130 operator, and location determination system 140 operator, respectively.
The example user computing device 110 includes a user interface 111, an application 113, a data storage unit 115, an antenna 117, and a microphone assembly 119. In an example, the user computing device 110 communicates with the location determination system 140 via the network 120. In an example, the user computing device 110 broadcasts data via a wireless communication channel (e.g., wi-Fi communication channel 105) such that nearby access point devices 130 may receive the broadcast data via the wireless communication channel. In an example, the user computing device 110 receives data from one or more access point devices 130 associated with the location determination system 140 over a wireless communication channel (e.g., wi-Fi communication channel 105). In this example, the user computing device 110 transmits data to one or more access point devices 130 associated with the location determination system 140 over a wireless communication channel (e.g., wi-Fi communication channel 105).
In an example, the user interface 111 enables the user 101 to interact with the user computing device 110. For example, the user interface 111 includes a touch screen, a voice-based interface, or any other interface that allows the user 101 to provide input and receive output from the application 113 on the user computing device 110. In an example, the user 101 interacts with the application 113 via a user interface 111. In an example, the user 101 may log into the application 113 by selecting the application 113 via the user computing device 110 and/or entering the name and password of the user 101 via the user interface 111.
In an example, the application 113 is a program, function, routine, applet, or similar entity that resides on the user computing device 110 and performs its operations on the user computing device 110. In some examples, the user 101 must install the application 113 on the user computing device 110 and/or make feature selections to obtain the benefits of the techniques described herein. In an example, the user 101 accesses an application 113 on the user computing device 110 via the user interface 111. In an example, the application 113 is associated with a location determination system 140. In an example, the application 113 includes a payment application or a wallet application. In another example, the application 113 includes a ticketing application. In yet another example, the application 113 includes an email application, a map application, a shopping application, a social media application, or other application. In an example, when the user 101 logs into the application, the user computing device 110 broadcasts data, e.g., a user computing device 110 network identifier, via the Wi-Fi communication channel 105. In other examples, the user computing device 110 broadcasts data via the Wi-Fi communication channel 105 based on one or more configurations of the user computing device 110 set by the user 110 via the user interface 111. In some examples, the user computing device 110 does not include the application 113, or the user 101 does not download the application 113 onto the user computing device 110 via the network 120. In some examples, one or more functions described herein as being performed by the application 113 or performed via the application 113 are performed via the user computing device 110 operating system.
In an example, the data storage unit 115 includes a local or remote data storage structure accessible to the user computing device 110 that is adapted to store information. In an example, the data storage unit 115 stores encrypted information, such as HTML5 local storage.
In an example, antenna 117 is a means of communication between user computing device 110 and access point device 130. In an example embodiment, wi-Fi controller 119 outputs a radio signal through antenna 117 or listens for a radio signal from access point device 130. In another example embodiment, a bluetooth controller or a near field communication ("NFC") controller is used.
In an example, wi-Fi controller 119 is capable of sending and receiving data, performing authentication and encryption functions, and directing how user computing device 110 will listen for transmissions from access point device 130 or configure user computing device 110 into various power saving modes according to Wi-Fi specified procedures. In another example embodiment, the user computing device 110 includes a bluetooth controller or NFC controller capable of performing similar functions. The example Wi-Fi controller 119 communicates with the application 113 and is capable of sending and receiving data over the wireless Wi-Fi communication channel 105. In another example embodiment, the bluetooth controller 119 or NFC controller 119 performs similar functions as the Wi-Fi controller 119 using bluetooth or NFC protocols. In an example embodiment, wi-Fi controller 119 activates antenna 117 to create a wireless communication channel between user computing device 110 and access point device 130. The user computing device 110 communicates with the access point device 130 via an antenna 117. In an example embodiment, when the user computing device 110 has been activated, the Wi-Fi controller 119 polls for radio signals through the antenna 117 or listens for radio signals from the access point device 130.
The example access point device 130 includes an application 133, a data storage unit 135, an antenna 137, and a Wi-Fi controller 139. In some examples, access point device 130 includes a beacon device or a mobile computing device, such as a smart phone device, tablet device, or other mobile computing device. In an example, the access point device 130 receives data broadcast by one or more user computing devices 110 via the Wi-Fi communication channel 105. In other examples, the access point device 130 communicates data to the user computing device 110 via a Wi-Fi communication channel. In another example, access point device 130 communicates with user computing device 110 via network 120. In an example, the access point device 130 communicates with the location determination system 140 via the network 120 to send data to the location determination system 140. In an example, the access point device 130 communicates with the location determination system 140 via the network 120 to receive data from the location determination system 140. In an example, the location associated with the location determination system 140 includes a plurality of access point devices 130, the plurality of access point devices 130 in communication with the location determination system 140 via the network 120. In this example, each of the plurality of access point devices 130 receives data broadcast by the plurality of user computing devices 110 over the Wi-Fi communication channel 105.
In an example, the application 133 is a program, function, routine, applet, or similar entity that resides on the access point device 130 and performs its operations on the access point device 130. In certain example embodiments, an access point device 130 operator or other location determination system 140 must install an application 133 on the access point device 130 and/or make feature selections to obtain the benefits of the techniques described herein. In an example embodiment, an access point device 130 operator may access an application 133 on the access point device 130 via a user interface. In an example embodiment, the application 133 may be associated with a location determination system 140. In another example embodiment, the application 133 may be associated with a system that is otherwise associated with the access point device 130.
In an example, the data storage unit 135 comprises a local or remote data storage structure accessible to the access point device 130 suitable for storing information. In an example, the data storage unit 135 stores encrypted information, such as HTML5 local storage.
In an example, antenna 137 is a means of communication between access point device 130 and one or more user computing devices 110. In an example embodiment, wi-Fi controller 139 outputs radio signals through antenna 137 or listens for radio signals from one or more user computing devices 110 at a location. In another example embodiment, a bluetooth controller or a near field communication ("NFC") controller is used. In an example embodiment, wi-Fi controller 139 outputs radio signals through antenna 137 or listens for radio signals from one or more user computing devices 110. In an example, the antenna 137 includes an antenna array. In an example, the antenna 137 includes a rotated highly directional antenna 137, enabling the user computing device 110 to determine an angle of arrival ("AOA") of received Wi-Fi signal data.
In an example, wi-Fi controller 139 can send and receive data, perform authentication and encryption functions, and instruct access point device 130 how to listen for transmissions from one or more user computing devices 110 at a location or configure access point device 130 into various power saving modes according to Wi-Fi specified procedures. In another example embodiment, the access point device 130 includes a bluetooth controller or NFC controller capable of performing similar functions. The example Wi-Fi controller 139 communicates with the application 133 and is capable of sending and receiving data over the wireless Wi-Fi communication channel 105. In another example embodiment, bluetooth controller 139 or NFC controller 139 performs similar functions as Wi-Fi controller 139 using bluetooth or NFC protocols. In an example embodiment, wi-Fi controller 139 activates antenna 137 to create a wireless communication channel between each of the one or more user computing devices 110 and access point device 130. Access point device 130 communicates with user computing device 110 via antenna 137. In an example embodiment, wi-Fi controller 139 polls for radio signals through antenna 137 or listens for radio signals from one or more user computing devices 110 when access point device 130 has been activated.
The example location determination system 140 includes a location determination component 143 and a data storage unit 145. In an example, the location determination system 140 communicates with the user computing device 110 and the access point device 130 via the network 120.
In an example, the location determination component 143 receives wireless signal attribute data from one or more access point devices 130 via the network 120. In an example, the location determination component 143 classifies each of the one or more user computing devices 110 as "mobile" or "stationary" based on received wireless signal attribute data from the one or more access point devices 130. In an example, the location determination component 143 calculates a location for each user computing device 110 classified as "mobile" and does not calculate a location for each user computing device 110 classified as "stationary.
In an example, the data storage unit 145 includes a local or remote data storage structure accessible to the position-determining system 140 that is adapted to store information. In an example, the data storage unit 145 stores encrypted information, such as HTML5 local storage.
In an example, the network computing device and any other computing machine associated with the techniques presented herein may be any type of computing machine, such as, but not limited to, those discussed in more detail with respect to fig. 8. Additionally, any function, application, or component associated with any of these computing machines (such as those described herein, or any other (e.g., script, web content, software, firmware, hardware, or modules) associated with the techniques presented herein) may be any of the components discussed in more detail with respect to fig. 8. The computing machines discussed herein may communicate with each other and with other computing machines or communication systems over one or more networks, such as network 120. Network 120 may include any type of data or communication network, including any of the network technologies discussed with respect to fig. 8.
Example procedure
The example methods illustrated in fig. 2-5 are described below with respect to components of the example operating environment 100. The example methods of fig. 2-5 may also be performed with other systems and in other environments. The operations described with respect to any of fig. 2-5 may be implemented as executable code stored on a computer or machine-readable non-transitory tangible storage medium (e.g., floppy disk, hard disk, ROM, EEPROM, non-volatile RAM, CD-ROM, etc.), which operations are performed by processor circuitry implemented using one or more integrated circuits based on execution of the code; operations described herein may also be implemented as executable logic (e.g., a programmable logic array or device, field programmable gate array, programmable array logic, application specific integrated circuits, etc.) encoded in one or more non-transitory tangible media for execution.
Fig. 2 is a block diagram depicting a method 200 in accordance with certain examples, the method 200 for determining a location of a user computing device 110 from a motion classification based on wireless signal data received at an access point device 130. The method 200 is described with reference to the components shown in fig. 1.
In block 210, the enterprise system configures access point devices 130 at the enterprise location. In an example, the enterprise location includes a multi-level office building, a transfer station, a school, a stadium, a train, an airplane, a ship, or other location associated with the enterprise system and including a plurality of access point devices 130. In an example, the enterprise system configures the access point device 130 to communicate with the location determination system 140 over the network 120. Additionally, in this example, the enterprise system configures the access point device 130 to receive data at the enterprise location via the Wi-Fi communication channel 105. For example, the access point device 130 is configured to receive Wi-Fi signal data broadcast by one or more user computing devices 110 at an enterprise location. In an example, the access point device 130 is configured to transmit the received Wi-Fi signal data to the location determination system 140 via the network 120. In some examples, the enterprise system includes a location determination system 140 or otherwise communicates with the location determination system 140 via the network 120. In an example, each access point device 130 includes a respective access point device 130 identifier that identifies the respective access point device 130 and each access point device 130 is associated with a particular location within the enterprise location where the respective access point device 130 is located. In an example, the enterprise system accesses the location determination system 140 website and downloads the application 133 on each access point device 130. In other examples, the enterprise system purchases or otherwise obtains access point devices 130 from the location determination system 140, and the access point devices 130 already have applications 133 pre-installed on each access point device 130. In an example, the application 133 communicates with the location determination system 140 via the network 120.
In block 220, the user 101 configures the user computing device 110 to broadcast data via the wireless communication channel 105. A method for configuring the user computing device 110 to broadcast data via the wireless communication channel 105 is described in more detail below with reference to the method described in fig. 3. In an example, a plurality of users 101 configure respective user computing devices 110 using the method described in fig. 3 and enter enterprise locations using the user computing devices 110. For example, multiple employees at an enterprise location use the method described in FIG. 3 to configure corresponding employer-provided user computing devices 110.
Fig. 3 is a block diagram depicting a method 220 for configuring a user computing device 110 to broadcast data via a wireless communication channel 105, in accordance with certain examples. The method 220 is described with reference to the components shown in FIG. 1.
In block 310, the user 101 accesses the location determination system 140 website via the user computing device 110. In an example, the user 101 inputs the website 143 address into the web browser 112 of the user computing device 110 or otherwise accesses the website 143 via the user interface 111 of the user computing device 110. In an example, the user 101 drives a user interface 111 object on an advertisement on the web browser 112 and the web browser 112 redirects to the website 143.
In block 320, the user 101 registers for a user 101 account via the location determination system 140 website. The user 101 may obtain the user 101 account, receive appropriate applications and software to install on the user computing device 110, request authorization to participate in the transaction process, or perform any actions required by the location determination system 140. The user 101 may utilize the functionality of the user computing device 110, such as the user interface 111 and web browser, to register and configure the user 101 account. In an example, the user 101 may enter payment account information (e.g., one or more credit accounts, one or more bank accounts, one or more stored value accounts, and/or other suitable accounts) associated with one or more user 101 accounts into the user 101 accounts maintained by the location determination system 140. In an example, the location determination system 140 generates a network identifier for the user computing device 110, associates the network identifier with the respective user computing device 110, and sends the network identifier to the application 113 of the user computing device 110 via the network 120.
In block 330, the user 101 uploads account information to the user 101 account. In an example, the user 101 may configure account settings of the user 101 or add, delete or edit payment account information via the location determination system website 143. In an example, the user 101 may select an option to enable or disable permissions for the location determination system 140 to process transactions. For example, the payment account information includes: an account number, expiration date, address, user 101 account holder name, or other information associated with the user 101 payment account that will enable the location determination system 140 to process the payment transaction.
In block 340, the user 101 downloads the application 113 onto the user computing device 110. In an example, the user 101 selects an option on the location determination system 140 website 143 to download the advertisement application 113 onto the user computing device 110. In an example, the advertisement application 113 running on the user computing device 110 is capable of communicating with the location determination system 140 over the network 120. In an example, when the user 101 logs into the advertisement application 113, the advertisement application 113 running on the user computing device 110 can communicate with the location determination system 140 over the network 120.
In block 350, the user 101 logs into the application 113 at the enterprise location. For example, the user 101 selects the application 113 via the user computing device 110 to log into the application 113. In another example, the user 101 selects an application via the user interface 111 and enters a user name and password. In an example, the user 101 logs into the application 113 before entering the enterprise location, after entering the enterprise location, or upon entering the enterprise location.
In block 360, the user computing device 110 broadcasts data at the enterprise location. For example, the user computing device 110 transmits radio frequency signals including Wi-Fi signal data in a scan of the access point device 130. In some examples, the radio frequency signal data includes one or more of the following: wi-Fi signal data, bluetooth low energy ("BLE") signal data, near field communication ("NFC") signal data, and other signal data. In an example, in response to the user 101 logging into the application 113, the application broadcasts Wi-Fi signal data for the user computing device 110 at the enterprise location via the Wi-Fi communication channel 105. In an example, the user computing device 110 broadcasts Wi-Fi signal data including a network identifier of the user computing device 110. In an example, the user computing device 110 network identifier was previously generated by the location determination system 140. In an example, the user computing device 110 broadcasts Wi-Fi signal data at the enterprise location via the wireless communication channel 105 such that other computing devices at the enterprise location, including one or more access point devices 130, may receive the broadcasted Wi-Fi signal data.
In some examples, the user 101 does not download the application 113 onto the user computing device 110 and does not log into the application 113 to cause the user computing device 110 to broadcast data. In some examples, user 101 configures one or more settings of user computing device 110 via user interface 111 such that user computing device 110 broadcasts Wi-Fi signal data via wireless communication channel 105 at the enterprise location. In other examples, the user computing device 110 is configured to continuously or periodically broadcast Wi-Fi signal data according to one or more default settings.
From block 360, the method 220 proceeds to block 230 in fig. 2.
Returning to fig. 2, in block 230, the location determination system 140 receives wireless signal attributes of the user computing device 110 from the access point device 130. The method 230 of receiving wireless signal data of the user computing device 110 from the access point device 130 by the location determination system 140 is described in more detail below with reference to the method described in fig. 4.
Fig. 4 is a block diagram of a method 230 of receiving wireless signal data of a user computing device 110 from an access point device 130 by a position determination system 140, according to some examples. The method 230 is described with reference to the components shown in fig. 1. The methods described herein are from the perspective of a single access point device 130. However, in some examples, the plurality of access point devices 130 receive Wi-Fi signal data from the one or more user computing devices 110 via the Wi-Fi communication channel 105 and retransmit the Wi-Fi signal data received from the one or more user computing devices 110 to the location determination system 140 via the network 120.
In block 410, the access point device 130 receives Wi-Fi signal data from the user computing device 110 via the Wi-Fi communication channel 105. For example, when access point device 130 has been activated, wi-Fi controller 139 polls for radio signals through antenna 137 or listens for radio signals broadcast by one or more user computing devices 110. Example Wi-Fi signal data includes a user computing device 110 network identifier. For example, the user computing device 110 network identifier is "userdevice1". In an example, the access point device 130 generates a timestamp associated with the time that Wi-Fi signal data was received from the user computing device 110 and associates the timestamp with the user computing device 110 network identifier. For example, the timestamp includes one or more of the following: month, day of the month, year, hour of the day, minute of the hour, second of the minute, millisecond of the second, and other suitable time metrics. For example, wi-Fi signal data is received from the user computing device 110 at GMT time of 6 months 12 days of 2018, 6:35:03 afternoon, and the timestamp is written "18:35:03 of 12 days of 6 months of 2018". In an example, the access point device 130 determines available Wi-Fi signal data at each time interval t. For example, the time interval t is one second, two seconds, thirty seconds, one minute, two minutes, or other suitable length of time interval t. The higher time interval t at which the access point device 130 is configured to determine the available Wi-Fi signal data reduces the accuracy of the location determination, while the lower time interval t at which the access point device 130 is configured to determine the available Wi-Fi signal data increases the accuracy of the location determination. For example, the time interval is one second, and the access point device 130 receives Wi-Fi signal data including network identifiers "userdevice1" and "userdevice" from the user computing device 110.
In block 420, the access point device 130 determines wireless signal attributes of the user computing device 110 based on Wi-Fi signal data received from the user computing device 110. For example, the access point device 130 determines one or more of the following: a received signal strength indicator ("RSSI"), an angle of arrival ("AoA"), a time of arrival ("TOA"), a time difference of arrival ("TDOA"), or other related wireless signal attributes. In an example, determining the wireless signal attribute includes: the difference in the phase values (phase vectors) received at each antenna 137 in the array of antennas 137 is determined based on Wi-Fi signal data received from the user computing device 110. In some examples, the access point device 130 determines the second wireless signal attribute from the first wireless signal attribute. For example, the access point device 130 determines the AoA from the phase vector data. For example, the access point device 130 receives Wi-Fi signal data from the user computing device 110 identified by the user computing device 110 network identifier "userdevice 1". In this example, access point device 130 determines that the received Wi-Fi signal data broadcast by user computing device 110 includes a phase vector comprising thirty-two phase values measured at thirty-two circular array antennas of access point device 130. In this example, the phase vector may include another suitable number of phase values measured at a corresponding number of circular array antennas of access point device 130. In an example, the access point device 130 determines "n=i" phase vector values comprising [ n 1,n2,…ni ], where the values are in the range of 0< n < 2pi, where pi=3.14159. For example, access point device 130 determines n phase vector values including [0.5,0.6,..0.57 ] based on the received Wi-Fi signal data. Each phase value represents the phase of a signal arriving at a particular antenna of the circular antenna array of access point device 130. In some examples, the antenna array may include another suitable antenna array configuration in addition to a circular antenna array. In an example, the access point device 130 determines an angle of arrival ("AoA") of the received Wi-Fi signal data broadcast by the user computing device 110 based on the determined phase vector data. In an example, the AoA may be represented in radians, degrees, or other suitable angular amounts. For example, the access point device 130 determines a 1.4 radian AoA based on the determined phase vector data for the received Wi-Fi signal data broadcast by the user computing device 110. In an example, the access point device 130 determines a received signal strength indicator ("RSSI") of-40 dBm based on received Wi-Fi signal data broadcast by the user computing device 110. In an example, the RSSI values range between-120 dBm to 0dBm, 0dBm indicating the strongest received signal strength and-120 dBm indicating the weakest received signal strength.
In block 430, the access point device 130 sends the wireless signal attributes of the user computing device 110 to the location determination system 140 via the network 120. For example, the access point device 130 sends the determined wireless signal attributes of the user computing device 110, the recorded time stamp, and the user computing device 110 network identifier to the location determination system 140 via the network 120. Example wireless signal attributes include one or more of phase vector data, aoA, and RSSI determined from received Wi-Fi signal data broadcast by the user computing device 110. Example wireless signal attributes also include a user computing device 110 network identifier. The example wireless signal attributes also include an access point device 130 identifier associated with the access point device 130. For example, the access point device 130 sends wireless signal attributes including "2018, 6, 12, 18:35:03, userdevice1, rssi= -40, aoa=1.2" to the location determination system 140 via the network 120. In an example, the access point device 130 also transmits the access point device 130 network identifier (e.g., "ap 1") to the location determination system 140 via the network 120 as part of the wireless signal attribute data. For example, the access point device 130 transmits wireless signal attribute data including the access point device 130 network identifier, including "2018, 6, 12, 18:35:03, userdevices 1, rssi= -40, aoa = 1.2, ap1". In an example, the access point device 130 includes phase vector data as part of the wireless attribute data. For example, access point device 130 transmits wireless signal attribute data comprising "2018, month 6, 12, day 18:35:03, userdevice1, rssi= -40, aoa = 1.2, [0.13,0.15,0.35..0.57 ]," where [0.13,0.15,0.35..0.57 ] includes a phase vector value for each of n antennas of a circular antenna array of access point device 130.
In block 440, the location determination system 140 receives wireless signal attributes of the user computing device 110 from the access point device 130 via the network 120. In an example, the location determination system 140 includes a location determination component 143, the location determination component 143 storing the received wireless signal attributes in a database in a data storage unit 145 accessible to the location determination component 143. In an example, the location determination component 143 stores wireless signal attributes based on the user computing device 110 network identifier and the access point device 130 network identifier. For example, the location determination system 140 receives wireless signal attributes of the user computing device 110 (including "2018 month 12 day 18:35:03, userdevice1, RSSI = -40, aoA = 1.2, ap 1") from the access point device 130 and stores the received wireless signal attributes "RSSI = -40", "AoA = 1.2", time "2018 month 12 day 18:35:03" under the user computing device 110 network identifier "userdevice1" and under the access point device 130 network identifier "ap 1". In an example, the location determination system 140 periodically or continuously receives subsequent wireless signal attributes of the user computing device 110 from the access point device 130 via the network 120. For example, the location determination system 140 receives subsequent wireless signal attributes of the user computing device 110 every one second, every five seconds, or at another suitable interval.
From block 440, the method 230 proceeds to block 240 in fig. 2.
The example method 230 describes receiving Wi-Fi signal data of a single user computing device 110 from a single access point device 130. However, in the example, the location determination system 140 receives wireless signal attributes of the plurality of user computing devices 110 at the enterprise location from the plurality of access point devices 130 at the enterprise location using the example method 230. In an example, the location determination system 140 receives wireless signal attributes of a plurality of user computing devices 110 at an enterprise location from a plurality of access point devices 130 at the enterprise location periodically over a subsequent time interval using the example method 230. In some examples, each of the plurality of access point devices 130 is synchronized to transmit, at each subsequent time interval, wireless signal attributes of one or more user computing devices 110 for which the respective access point device 130 received Wi-Fi signal data over a previous time interval. Example time intervals include one second, two seconds, five seconds, or other suitable time intervals.
Returning to fig. 2, in block 240, the location determination system 140 receives wireless signal attributes of the user computing device 110 over a next time window from one or more access point devices 130. In an example, the location determination system 140 receives wireless signal attributes of a plurality of user computing devices 110 at an enterprise location from a plurality of access point devices 130 at the enterprise location using the example method 230. In this example, each access point device 130 sends wireless signal attributes of one or more user computing devices 110 to the location determination system 140 using the example method 230. In this example, the location determination system 140 records the received wireless signal attributes in a database based on the user computing device 110 network identifier and the access point device 130 network identifier. Additionally, in an example, the location determination system 140 organizes wireless signal attribute data associated with the network identifier of the particular user computing device 110 in a database according to the time stamp.
In block 250, the location determination system classifies the user computing device 110 as stationary or moving over the current time window. The method 250 of determining a motion classification of the user computing device 110 based on wireless signal data of the user computing device 110 over a time window by the position determination system 140 is described in more detail below with reference to the method described in fig. 5. In an example, the current time window includes one or more time intervals for which wireless signal data for one or more user computing devices 110 is received via network 120 from one or more access point devices 130 from the enterprise location.
The example method 250 describes a location determination system 140, the location determination system 140 determining a motion classification for a single user computing device 110 at an enterprise location based on wireless signal attribute data received from one or more access point devices 130 at the enterprise location for the user computing device 110 over a current time window. In some examples, the location determination system 140 determines a motion classification for two or more user computing devices 110 at the enterprise location based on wireless signal attribute data of the two or more user computing devices 110 over a current time window received from one or more access point devices 130 at the enterprise location. For example, as previously discussed, in an example, the location determination system 140 receives wireless signal attributes of a plurality of user computing devices 110 at an enterprise location from a plurality of access point devices 130 at the enterprise location using the example method 230. In this example, each access point device 130 sends wireless signal attributes of one or more user computing devices 110 to the location determination system 140 using the example method 230. In this example, the location determination system 140 records the received wireless signal attributes in a database based on the user computing device 110 network identifier and the access point device 130 identifier.
Fig. 5 is a block diagram depicting a method 250 in accordance with certain examples, the method 250 for determining, by the location determination system 140, a motion classification of the user computing device 110 based on wireless signal data of the user computing device 110 over a time window. The method 250 is described with reference to the components shown in FIG. 1.
In block 510, the location determination system 140 extracts wireless signal data of the user computing device 110 received from each access point device 130. For example, the location determination component 143 retrieves all wireless signal attribute data associated with the particular user computing device 110 network identifier at various points in time from a database in the data store 145 and organizes the retrieved wireless signal attribute data for the user computing device 110 according to the access point device 130 network identifier and/or according to a timestamp. For example, over an example time window 18:35:10-18:35:20 of five second time intervals, for a single user computing device 110 associated with user computing device 110 network identifier "userdevice1", the organized data includes:
"userdevice1, ap1, 18:35:10, rssi= -40, [ phase vector data 1]",
"Userdevice1, ap1, 18:35:15, rssi= -39, [ phase vector data 2]",
"Userdevice1, ap1, 18:35:20, rssi= -14, [ phase vector data 3]",
"Userdevice1, ap2, 18:35:10, rssi= -12, [ phase vector data x ]",
"Userdevice1, ap2, 18:35:15, rssi= -13, [ phase vector data y ]",
"Userdevice1, ap2, 18:35:20, rssi= -12, [ phase vector data z ]".
In this example, each of example phase vector data 1, phase vector data 2, phase vector data 3, phase vector data x, phase vector data y, and phase vector data z includes a phase value detected for Wi-Fi signal data of user computing device 110 at a time associated with a particular timestamp for each antenna of the circular array of antennas of access point device 130. In some examples, the wireless signal data of the user computing device 110 is organized differently, e.g., first according to a timestamp, then according to the access point device 130 identifier. In some examples, the location determination system 140 does not have a network identifier for each access point device 130 or wireless signal data for all user computing devices 110 at each time interval t. For example, not every access point device 130 may receive Wi-Fi signal data from the user computing device 110 at any particular time interval t, as shown in fig. 6. For example, the access point device 130 may not be able to determine RSSI, one or more phase vector values of the phase vector data, or AoA based on Wi-Fi signal data broadcast by the user computing device 110 for each time interval of the current time window. In another example, the access point device 130 does not receive Wi-Fi signal data from the user computing device 110 for one or more time intervals of the current time window.
In another example, for each time interval over the time window w, the location determination system 140 extracts wireless signal data for a particular access point device 130, for a particular user computing device 110 network identifier. For example, for the user computing device 110 corresponding to network identifier "userdevice1", for wireless signal data received from the access point device 130 corresponding to network identifiers "ap2", "ap3", and "ap5", the location determination system 140 extracts the following wireless signal data:
"userdevice1, ap2, 18:35:15, rssi= -40, [ phase vector data y ]",
"Userdevice1, ap2, 18:35:20, rssi= -35, [ phase vector data z ]",
"Userdevice1, ap3, 18:35:15, rssi= -40, [ phase vector data a ]",
"Userdevice1, ap3, 18:35:20, rssi= -35, [ phase vector data b ]",
"Userdevice, ap5, 18:35:15, rssi = no data, [ no phase vector data ]",
"Userdevice, ap5, 18:35:20, rssi= -35, [ phase vector data n ]".
In this example, the access point device 130 labeled "ap5" does not transmit any wireless signal data to the position determination system 140 at time 18:35:15.
Fig. 6 is a diagram illustrating an example configuration of an example user computing device 110 and a plurality of access point devices 130 at an example enterprise location at time T. Fig. 6 illustrates an example user computing device 110 and six example access point devices 130, labeled A, B, C, D, E and F, at various locations within an enterprise location at time T. In this example illustration, the location of the user computing device 110 is such that only the access point device A, B, C, D receives Wi-Fi signal data broadcast by the user computing device via the Wi-Fi communication channel 105 at time T. In this example illustration, access point devices E and F do not receive Wi-Fi signal data of user computing device 110 at time T. For example, access point devices E and F are not within a predefined threshold range necessary to receive Wi-Fi signal data from user computing device 110 at time T, and access point devices A, B, C and D are within the predefined threshold range. In another example, one or more of access point devices E and F are disabled or otherwise not receive Wi-Fi signal data from user computing device 110 via a Wi-Fi communication channel.
Returning to fig. 5, in block 520, the location determination system 140 extracts characteristics of the wireless signal data of the user computing device 110 received from each access point device 130 at the enterprise location.
As previously discussed, in an example, each access point device 130 at an enterprise location is configured to transmit wireless signal data to the location determination system 140 via the network 120. Each access point device 130 receives Wi-Fi signal data from each of the one or more user computing devices 110 via the wireless communication channel 105 at each time interval t over the time window w, determines wireless signal attribute data based on the received Wi-Fi signal data, and transmits the determined wireless signal attribute data to the location determination system 140 via the network 120. For example, the time interval t is one second, two seconds, thirty seconds, one minute, two minutes, or other suitable length of time interval t. The higher time interval t at which the access point device 130 is configured to determine the available Wi-Fi signal data reduces the accuracy of the location determination, while the lower time interval t at which the access point device 130 is configured to determine the available Wi-Fi signal data increases the accuracy of the location determination.
In an example, the location determination system 140 aggregates wireless signal characteristics for one or more access point devices 130 that describe wireless signal characteristics of the user computing device 110 between a most recent time interval t and one or more previous time intervals. For example, for access point device 130, location determination system 140 aggregates wireless signal characteristics associated with a particular user computing device 110 over one or more of time intervals t, t-1, t-2, t-3, t-4, t-5, t-6, t-7, t-8, t-9, and t-10. In this example, the location determination system 140 may not have one or more wireless signal characteristics for one or more of the time intervals t, t-1, t-2, t-3, t-4, t-5, t-6, t-7, t-8, t-9, and t-10 for one or more of the access point device 130 network identifiers.
In some examples, the position determination system 140 selectively selects wireless signal attributes (RSSI, phase vectors) from wireless signal data measured at each access point device 130 in a time window to calculate features from these attributes. For example, the position determination system 140 receives wireless signal properties (phase vectors) p (t), p (t-1), p (t-2), p (t-3) from the access point device 130 for a time window comprising four time intervals t, t-1, t-2, t-3. For example, the access point device 130 has eight antennas in a circular antenna array, and each phase vector has eight phase values, where "NA" represents an unmeasured phase value:
p(t)=[NA,0.5,NA,1.2,1.2,2.4,NA,1.1]
p(t-1)=[3.2,0.4,NA,1.1,NA,2.4,NA,NA]
p(t-2)=[NA,0.3,NA,1.3,1.2,2.1,NA,NA]
p(t-3)=[NA,NA,NA,NA,1.2,2.4,NA,1.1]
In this example, comparing the phase values measured by the access point device 130 at p (t-1) and p (t), the access point device 130 measured the phase vector values at both p (t-1) and p (t) at the following three antennas: antennas 2, 4 and 6. Comparing the phase values measured by the access point device 130 at p (t-2) and p (t), the access point device 130 measured the phase vector values at both p (t-2) and p (t) at the following four antennas: antennas 2, 4,5 and 6. Comparing the phase values measured by the access point device 130 at p (t-3) and p (t), the access point device 130 measured the phase vector values at both p (t-3) and p (t) at the following three antennas: antennas 5,6 and 8. In this example, the threshold number of phase values measured at the same antenna at access point 130 is 4 antennas to ensure high certainty of the phase vector correlation value. Thus, in this example, the wireless signal properties from time intervals t and t-2 are considered in calculating the phase correlation. In some examples, the position determination system 140 normalizes the phase correlation with the number of phase values measured at the corresponding antennas for which the phase values were measured for the selected time interval.
In one example, the position determination system 140 calculates a correlation value of one or more characteristics of the wireless signal data (e.g., one or more of an RSSI value calculated from the wireless signal data, an AoA value, phase vector data, and other data) between the current time interval t and time interval t-1 (the previous time interval). In another example, the position determination system 140 calculates correlations between one or more features for one or more previous time intervals and a recent time interval (e.g., for one or more of t-6, t-5, t-4, t-3, t-2, and t-1). For example, the correlation represents a similarity between radio signal characteristics from the following time intervals for which radio signal properties have been determined: a latest time interval, and two or more previous time intervals.
In an example, the phase vector correlation value may be determined as follows: which determines phase vector data for one or more previous time intervals And phase vector data for the latest time interval availableCorrelation between them. Other formulas may be used to calculate the correlation value of the wireless signal characteristic. In another example, to determine relevance, the location determination system 140 can compare the aggregate RSSI values of the most recent time interval for which wireless signal data of the user computing device 110 has been determined with the selected previous time interval. In yet another example, the position determination system 140 determines an absolute difference between the aggregate RSSI measured for two or more previous time intervals and the latest time interval. In this example, if aggregated RSSI data is not determined for one or more time intervals, then the time intervals will not be considered in determining the correlation. In yet another example, the position determination system 140 determines absolute differences between consecutive aggregate RSSI determined for two or more previous time intervals and the latest available time interval within a time window and then calculates the standard deviation of these differences. In yet another example, the position determination system 140 determines a correlation value comprising: the ratio between the number of time intervals of the available aggregate RSSI and the number of time intervals being considered in the time window is included.
In block 530, the location determination system 140 aggregates wireless signal characteristics over a time window for the plurality of access point devices 130, the time window including a most recent time interval and one or more time intervals preceding the most recent time interval. In an example, the position determination system 140 determines one or more phase vector correlations from each of the one or more access point devices 130 over a time window. The position determination system 140 determines an aggregate phase vector correlation that includes the maximum of the one or more phase vector correlations determined for the one or more access point devices 130.
In another example, the location determination system 140 determines a continuous RSSI at each of the one or more access point devices 130 over a time window and determines the following aggregate feature given the multiple values of the feature extracted from the multiple access point devices 130: the aggregate feature includes pearson correlations (Pearson correlation) for the pairs of consecutive RSSI's. In this example, the consecutive RSSI includes a pair of RSSI measured at the access point device 130 at the current time interval t and the previous time interval t-1. In another example, the location determination system 140 determines absolute differences between successive RSSI measurements at each of the one or more access point devices 130 over a time window, and the location determination system 140 determines an aggregated characteristic that includes an average of these determined absolute differences. In another example, the location determination system 140 determines the standard deviation of the RSSI measured at each of the one or more access point devices 130 over a time window and the location determination system 140 determines an aggregated feature comprising an average of these determined standard deviations. In another example, the location determination system 140 determines, for each of the one or more access point devices 130, a difference between consecutive RSSI's in the time window and then calculates an aggregated feature comprising an average of standard deviations of the determined differences. In yet another example, the location determination system 140 determines a number of available RSSI for each of the one or more access point devices 130 over a time window, and the location determination system 140 determines an aggregated characteristic that includes a ratio between the number of available RSSI and the time window size.
In block 540, the location determination system 140 determines a motion classification of the user computing device 110 over the last time window based on the aggregated wireless signal characteristics. For example, as previously described in block 530, the location determination system 140 determines the aggregated wireless signal characteristics. In some examples, the position determination system 140 determines an aggregate phase vector correlation, and a higher aggregate phase vector correlation indicates that the user computing device 110 is more likely to be stationary over a time window. Conversely, a lower aggregate phase vector correlation indicates that the user computing device 110 is more likely to have moved over a time window. In this example, if the aggregated phase vector meets or exceeds a threshold, the position determination system 140 determines that the motion classification of the user computing device over the time window is "stationary". In this example, if the aggregated phase vector is below a threshold, the position determination system 140 determines that the motion classification of the user computing device over the time window is "mobile".
In other examples, the location determination system 140 determines pearson correlations of consecutive RSSI measured at one or more access point devices 130 over a time window, and a higher pearson correlation indicates that the user computing device 110 is more likely to be stationary over the time window. Conversely, a lower pearson correlation indicates that the user computing device 110 is more likely to have moved over the time window. In this example, if the pearson correlation meets or exceeds the threshold, the position determination system 140 determines that the motion classification of the user computing device over the time window is "stationary". In this example, if the pearson correlation is below the threshold, the position determination system 140 determines that the motion classification of the user computing device over the time window is "mobile".
In other examples, the location determination system 140 determines an absolute difference between consecutive RSSI measured at each of the one or more access point devices 130 over a time window. In these examples, a higher absolute difference indicates that the user computing device is more likely to be stationary over the time window. Conversely, a lower absolute difference indicates that the user computing device is more likely to have moved over the time window. In this example, if the absolute difference meets or exceeds a threshold, the position determination system 140 determines that the motion classification of the user computing device over the time window is "stationary". In this example, if the absolute difference is below the threshold, the position determination system 140 determines that the motion classification of the user computing device over the time window is "mobile".
In other examples, the location determination system 140 determines an average of standard deviations of the RSSI measured at each of the one or more access point devices 130 over a time window. In these examples, a higher average value indicates that the user computing device is more likely to be stationary over a time window. Conversely, a lower average value indicates that the user computing device is more likely to have moved over a window of time. In this example, if the average meets or exceeds the threshold, the position determination system 140 determines that the motion classification of the user computing device over the time window is "stationary". In this example, if the average value is below the threshold value, the location determination system 140 determines that the motion classification of the user computing device over the time window is "mobile".
In other examples, the location determination system 140 determines an average of standard deviations of differences between consecutive RSSI in the time window for each of the one or more access point devices 130. In these examples, a higher average value indicates that the user computing device is more likely to be stationary over a time window. Conversely, a lower average value indicates that the user computing device is more likely to have moved over a window of time. In this example, if the average meets or exceeds the threshold, the position determination system 140 determines that the motion classification of the user computing device over the time window is "stationary". In this example, if the average value is below the threshold value, the location determination system 140 determines that the motion classification of the user computing device over the time window is "mobile".
In still other examples, the location determination system 140 determines a ratio between the number of available RSSI and the time window size. In these examples, a higher ratio indicates that the user computing device is more likely to be stationary over the time window. Conversely, a lower ratio indicates that the user computing device is more likely to have moved over the time window. In this example, if the ratio meets or exceeds the threshold ratio, the position determination system 140 determines that the motion classification of the user computing device over the time window is "stationary". In this example, if the ratio is below the threshold ratio, the position determination system 140 determines that the motion classification of the user computing device over the time window is "mobile".
In addition to determining motion based on correlation values across aggregated features of access point device 130, other example models may be used. For example, the location determination system 140 may use a Recurrent Neural Network (RNN), random Forest (RF), support Vector Machine (SVM), or Hidden Markov Model (HMM) to classify the user computing device 110 as mobile or stationary based on applying the aggregated wireless signal characteristics to an appropriate model. In an example end-to-end RNN approach, the RNN learns the temporal correlation of RSSI and phase vector data to perform motion classification. The example end-to-end RNN includes an RNN for LSTM blocks of each access point device 130 to capture time correlation of RSSI and phase vectors measured at the respective access point device 130. Additionally, in this example, the end-to-end RNNs include neurons that combine the output from each RNN corresponding to a respective access point device 130 to predict the motion classification of the user computing device 110. In some examples, the position determination system 140 may use one of the previously mentioned models to determine a correlation value over a time window and then determine a motion classification for the user computing device 110 based on the correlation value determined by the model.
Returning to FIG. 2, in block 260, the location determination system 140 classifies the user computing device 110 as stationary or moving over the current time window. As previously discussed, the location determination system 140 classifies the user computing device 110 as "mobile" or "stationary" based on the correlation values of the aggregated wireless signal characteristics associated with one or more access point devices 130 for the user computing device 110 over a time window. The relevance value indicates whether the device is moving. The more similar the wireless signal data is from the current time interval to the previous time interval, the greater the likelihood that the user computing device 110 is stationary. However, significant changes in the location of the user computing device 110 result in changes in the wireless signal data received from the plurality of access point devices 130. The RSSI is, for example, an indication of signal strength and may be used as a proxy measure of the distance of the user computing device 110 from the access point device 130, and a significant change in the RSSI indicates movement of the user computing device 110 closer to the access point device 130 or farther from the access point device 130. The AoA is, for example, an indication of an angular direction from which a wireless signal of the user computing device 110 is received relative to the access point device 130, and a change in the AoA indicates movement of the user computing device 110 relative to the access point device 130.
Additionally, as previously discussed, the location determination system 140 may classify the user computing device 110 as mobile or stationary based on applying aggregated wireless signal features to a Recurrent Neural Network (RNN), random Forest (RF), support Vector Machine (SVM), hidden Markov Model (HMM), or other suitable model.
If the location determination system 140 classifies the user computing device 110 as stationary over the current time window, the method 200 returns to block 240 and the location determination system 140 receives wireless signal attributes of the user computing device 110 over the next time window from the one or more access point devices 130. For example, if the location determination system 140 classifies the user computing device 110 as "stationary" over the current time window, the location determination system 140 does not calculate the location of the user computing device 110 and the method 240 is repeated. Not calculating the location of the user computing device 110 in response to the "stationary" classification and only in response to the "mobile" classification results in a reduced processing by the location determination system 140. In some examples, one or more user computing devices 110 are determined to be "stationary" over the current time window, and the location determination system 140 does not calculate a location for each of the respective user computing devices 110 that are classified as "stationary" over the current time window.
Returning to block 260, if the location determination system 140 classifies the user computing device 110 as moving over the current time window, the method 200 proceeds to block 270. For example, the location determination system 140 classifies the user computing device 110 as "mobile" over a current time window.
In block 270, the location determination system 140 calculates a location of the user computing device 110 for the current time window based on wireless signal attributes received from one or more access points. For example, if the location determination system 140 classifies the user computing device 110 as "mobile" over the current time window, the location determination system 140 calculates the location of the user computing device 110 and the method 240 is repeated. Calculating the location of the user computing device 110 in response to the "mobile" classification and not calculating the location of the user computing device 110 in response to the "stationary" classification results in a reduced processing by the location determination system 140. In some examples, one or more user computing devices 110 are determined to "move" over a current time window, and the location determination system 140 calculates a location for each of the respective user computing devices 110 that are classified as "moving" over the current time window. In an example, the location determination system 140 uses the latest AoA and RSSI for the current time window to calculate a location for the user computing device 110 or to determine the location of the user computing device 110 based on wireless signal attributes of the user computing device 110 received from one or more access point devices 130 via the network 120. In these examples, the position determination system 140 determines the AoA from the phase vector data.
In block 280, the location determination system 140 displays the currently calculated location of the user computing device 110 via a user interface. In an example, the location determination system 140 provides a display of the most recently calculated location of the user computing device 110 determined via the example method 200. In another example, the location determination system 140 sends an indication of the location of the one or more user computing devices 110 determined via the example method 200 to the one or more computing devices via the network 120. In an example, an operator of the location determination system 140 may interact with a user interface 111 display of the most recently calculated locations of one or more user computing devices 110 at the enterprise location. In some examples, the calculated locations of one or more user computing devices 110 may enable location-based services, such as asset location recording or zone notification. For example, when a particular user computing device 110 enters a particular area within an enterprise location, the location determination system 140 may notify computing devices associated with the enterprise location via the network 120.
In block 290, the location determination system 140 determines whether other wireless signal attributes of the user computing device 110 have been received from one or more access point devices 130. In some examples, the user computing device 110 continues to broadcast Wi-Fi signal data at the enterprise location via the wireless communication channel 105, and the one or more access point devices 130 receive the broadcast Wi-Fi signal data and retransmit wireless signal characteristics of the user computing device 110 to the location determination system 140 via the network 120. However, in another example, the user computing device 110 is outside the Wi-Fi broadcast range of any access point device 130 at the enterprise location, and the location determination system 140 does not receive any other wireless signal characteristics of the user computing device 110 from one or more access point devices 130 via the network 120. In another example, the user 101 logs out of the application 113 on the user computing device 110 or otherwise turns off the power to the user computing device 110, and the user computing device 110 stops broadcasting Wi-Fi signal data.
If the location determination system 140 has received other wireless signal attributes of the user computing device 110 from the one or more access point devices 130, the method 200 proceeds to block 240 and the location determination system 140 receives wireless signal attributes of the user computing device 110 from the one or more access point devices 130 over the next time window. For example, the location determination system 140 determines a classification of "mobile" or "stationary" and calculates a new location in response to determining the classification of "mobile" or does not calculate a new location in response to determining the classification of "stationary" of the user computing device 110 based on the example method 200.
Returning to block 290, if the location determination system 140 has not received other wireless signal attributes of the user computing device 110 from one or more access points, the method 200 proceeds to block 340 in fig. 3. For example, in FIG. 3, in block 340, a user logs into an application 113 on a user computing device 110 at an enterprise location. For example, the user 101 logs out of the application 113 or otherwise turns off power to the user computing device 110, leaves the enterprise location, returns to the enterprise location the next day, and then logs into the application 113 on the user computing device 110.
Other examples
Fig. 8 depicts a computing machine 2000 and a module 2050, according to some examples. The computing machine 2000 may correspond to any of the various computers, servers, mobile devices, embedded systems, or computing systems presented herein. The module 2050 may include one or more hardware or software elements configured to facilitate execution of the various methods and processing functions presented herein by the computing machine 2000. The computing machine 2000 may include various internal components or attached components such as a processor 2010, a system bus 2020, a system memory 2030, a storage medium 2040, an input/output interface 2060, and a network interface 2070 for communicating with a network 2080.
The computing machine 2000 may be implemented as a conventional computer system, an embedded controller, a laptop, a server, a mobile device, a smart phone, a set top box, a kiosk, a router or other network node, a vehicle information system, one or more processors associated with a television, a custom machine, any other hardware platform, or any combination or multiplicity thereof. Computing machine 2000 may be a distributed system configured to function using multiple computing machines interconnected via a data network or bus system.
Processor 2010 may be configured to execute code or instructions to perform the operations and functions described herein, manage request flows and address mappings, and perform computations and generate commands. Processor 2010 may be configured to monitor and control the operation of components in computing machine 2000. Processor 2010 may be a general purpose processor, processor core, multiprocessor, reconfigurable processor, microcontroller, digital signal processor ("DSP"), application specific integrated circuit ("ASIC"), graphics processing unit ("GPU"), field programmable gate array ("FPGA"), programmable logic device ("PLD"), controller, state machine, gating logic, discrete hardware components, any other processing unit, or any combination or multiple thereof. Processor 2010 may be a single processing unit, a plurality of processing units, a single processing core, a plurality of processing cores, a dedicated processing core, a coprocessor, or any combination thereof. According to some embodiments, the processor 2010 and other components of the computing machine 2000 may be virtualized computing machines executing within one or more other computing machines.
The system memory 2030 may include a non-volatile memory such as read only memory ("ROM"), programmable read only memory ("PROM"), erasable programmable read only memory ("EPROM"), flash memory, or any other device capable of storing program instructions or data with or without the application of power. The system memory 2030 may also include volatile memory such as random access memory ("RAM"), static random access memory ("SRAM"), dynamic random access memory ("DRAM"), and synchronous dynamic random access memory ("SDRAM"). Other types of RAM may also be used to implement the system memory 2030. The system memory 2030 may be implemented using a single memory module or multiple memory modules. Although the system memory 2030 is depicted as being part of the computing machine 2000, those skilled in the art will recognize that the system memory 2030 may be separate from the computing machine 2000 without departing from the scope of the subject technology. It should also be appreciated that the system memory 2030 may include or may operate in conjunction with a non-volatile storage device such as storage medium 2040.
The storage medium 2040 may include: a hard disk, a floppy disk, a compact disk read-only memory ("CD-ROM"), a digital versatile disk ("DVD"), a blu-ray disk, a magnetic tape, a flash memory, other nonvolatile memory devices, a solid state drive ("SSD"), any magnetic storage device, any optical storage device, any electrical storage device, any semiconductor storage device, any physical-based storage device, any other data storage device, or any combination or multiplicity thereof. Storage media 2040 may store one or more operating systems, application programs, and program modules (such as module 2050), data, or any other information. The storage medium 2040 may be part of the computing machine 2000 or may be connected to the computing machine 2000. The storage medium 2040 may also be part of one or more other computing machines in communication with the computing machine 2000, such as a server, database server, cloud storage, network attached storage, and the like.
The module 2050 may include one or more hardware or software elements configured to facilitate execution of the various methods and processing functions presented herein by the computing machine 2000. The module 2050 may include one or more sequences of instructions stored as software or firmware in association with the system memory 2030, the storage medium 2040, or both. Storage medium 2040 may thus represent an example of a machine or computer-readable medium on which instructions or code may be stored for execution by processor 2010. A machine or computer readable medium may generally refer to any one or more of the media used to provide instructions to processor 2010. Such machine or computer-readable media associated with module 2050 may include a computer software product. It is to be appreciated that the computer software product comprising the module 2050 may also be associated with one or more processes or methods for delivering the module 2050 to the computing machine 2000 via the network 2080, any signal bearing medium, or any other communication or delivery technique. The module 2050 may also include hardware circuitry or information for configuring hardware circuitry (such as microcode or configuration information for an FPGA or other PLD).
The input/output ("I/O") interface 2060 may be configured to couple to one or more external devices to receive data from and transmit data to the one or more external devices. Such external devices and various internal devices may also be referred to as peripheral devices. The I/O interface 2060 may comprise both electrical and physical connections for operably coupling various peripheral devices to the computing machine 2000 or the processor 2010. The I/O interface 2060 may be configured to communicate data, address, and control signals between peripheral devices, the computing machine 2000, or the processor 2010. The I/O interface 2060 may be configured to implement any standard interface, such as a small computer system interface ("SCSI"), serial attached SCSI ("SAS"), fiber channel, peripheral component interconnect ("PCI"), PCI Express (PCIe), serial bus, parallel bus, advanced technology attachment ("ATA"), serial ATA ("SATA"), universal serial bus ("USB"), thunderbolt, fireWire, various video buses, and so forth. The I/O interface 2060 may be configured to implement only one interface or bus technology. Alternatively, the I/O interface 2060 may be configured to implement a variety of interface or bus techniques. The I/O interface 2060 may be configured as part of the system bus 2020, as a whole of the system bus 2020, or as operating in conjunction with the system bus 2020. The I/O interface 2060 may comprise one or more buffers that are used to buffer transmissions between one or more external devices, internal devices, the computing machine 2000, or the processor 2010.
The I/O interface 2060 may couple the computing machine 2000 to various input devices including a mouse, a touch screen, a scanner, an electronic digitizer, a sensor, a receiver, a touchpad, a trackball, a camera, a microphone, a keyboard, any other pointing device, or any combination thereof. The I/O interface 2060 may couple the computing machine 2000 to various output devices including video displays, speakers, printers, projectors, tactile feedback devices, automatic controls, robotic components, actuators, motors, fans, solenoids, valves, pumps, transmitters, signal transmitters, lights, and so forth.
The computing machine 2000 may operate in a networked environment using logical connections via a network interface 2070 to one or more other systems or computing machines across a network 2080. The network 2080 may include a Wide Area Network (WAN), a Local Area Network (LAN), an intranet, the internet, a wireless access network, a wired network, a mobile network, a telephone network, an optical network, or a combination thereof. The network 2080 may be packet-switched, circuit-switched, have any topology, and may use any communication protocol. The communication links within the network 2080 may involve various digital or analog communication media, such as fiber optic cables, free-space optics, waveguides, electrical conductors, wireless links, antennas, radio-frequency communications, and the like.
The processor 2010 may be connected to other elements of the computing machine 2000 or to various peripheral devices discussed herein through a system bus 2020. It will be appreciated that system bus 2020 may be internal to processor 2010, external to processor 2010, or both. According to certain examples, the processor 2010, other elements of the computing machine 2000, or any of the various peripheral devices discussed herein may be integrated into a single device such as a system on a chip ("SOC"), system on package ("SOP"), or ASIC device.
Where the system discussed herein gathers personal information about a user or may utilize personal information, the user may be provided with the following opportunities or options: whether or not the program or feature gathers user information (e.g., information about the user's social network, social actions or activities, profession, user preferences, or the user's current location), or whether and/or how to receive content from the content server that may be more relevant to the user. Furthermore, prior to storing or using certain data, the certain data may be processed in one or more ways such that personally identifiable information is removed. For example, the identity of the user may be processed such that personally identifiable information cannot be determined for the user, or the geographic location of the user may be summarized in the case where location information is obtained (such as at a city, zip code, or state level) such that a particular location of the user cannot be determined. Thus, the user can control how information about the user is collected and how the content server uses the information.
Embodiments may include a computer program embodying the functions described and illustrated herein, where the computer program is implemented in a computer system comprising instructions stored in a machine-readable medium and a processor executing the instructions. It should be apparent, however, that there are many different ways in which embodiments may be implemented in computer programming, and that these embodiments should not be construed as limited to any one set of computer program instructions. Additionally, a skilled programmer would be able to write such a computer program to implement embodiments of the disclosed embodiments based on the associated descriptions in the accompanying flowcharts and application text. Thus, the disclosure of a particular set of program code instructions is not considered necessary to provide a thorough understanding of how to make and use the embodiments. In addition, those skilled in the art will recognize that one or more aspects of the embodiments described herein may be performed by hardware, software, or a combination thereof, as may be embodied in one or more computing systems. Moreover, any reference to an action performed by a computer should not be construed as being performed by a single computer because more than one computer may perform the action.
Examples described herein may be used with computer hardware and software that perform the methods and processing functions described herein. The systems, methods, and processes described herein may be embodied in a programmable computer, computer-executable software, or digital circuitry. The software may be stored on a computer readable medium. For example, the computer readable medium may include: floppy disk, RAM, ROM, hard disk, removable media, flash memory, memory sticks, optical media, magneto-optical media, CD-ROMs, and the like. The digital circuitry may include integrated circuits, gate arrays, building block logic, field Programmable Gate Arrays (FPGAs), and the like.
The example systems, methods, and acts described in the previously set forth embodiments are illustrative, and in alternative embodiments, certain acts may be performed in a different order, in parallel with one another, omitted entirely, and/or combined between different examples, and/or certain additional acts may be performed. Accordingly, such alternative embodiments are included within the scope of the following claims, which are to be given the broadest interpretation so as to encompass such alternative embodiments.
Although specific embodiments have been described in detail above, this description is for illustrative purposes only. Accordingly, it should be appreciated that many of the aspects described above are not intended to be required or essential elements unless explicitly stated otherwise. Modifications to the disclosed aspects of the examples, as well as equivalent components or acts corresponding thereto, may be made by those of ordinary skill in the art having the benefit of the present disclosure without departing from the spirit and scope of the embodiments as defined in the appended claims, the scope of which is to be accorded the broadest interpretation so as to encompass such modifications and equivalent structures.

Claims (20)

1. A method of determining a location of a user computing device based on wireless signal attribute data and motion classification, comprising:
Receiving, by one or more computing devices, wireless signal data associated with a user computing device from a plurality of access point computing devices for a time window, the time window comprising at least a first time interval and one or more second time intervals preceding the first time interval;
Extracting, by the one or more computing devices, one or more features of the wireless signal data associated with the user computing device at the first time interval and at the one or more second time intervals preceding the first time interval for each of the plurality of access point computing devices;
Determining, by the one or more computing devices, an aggregate feature based on the extracted one or more features of the wireless signal data received from the plurality of access point computing devices;
Classifying, by the one or more computing devices, the user computing device as mobile based on the determined aggregated features, and
Responsive to classifying the user computing device as mobile, determining, by the one or more computing devices, a location of the user computing device based on the received wireless signal attribute data; and
The determined location of the user computing device is sent by the one or more computing devices to a second computing device for display via the second computing device.
2. The method of claim 1, wherein the wireless signal data comprises one or more of: a received signal strength indicator and a phase vector.
3. The method of claim 2, further comprising: an angle of arrival is determined by the one or more computing devices based on the received phase vector, and wherein the location of the user computing device is determined based on the determined angle of arrival and the received signal strength indicator.
4. A method as claimed in any one of claims 1 to 3, wherein the wireless signal data comprises one or more of: wi-Fi signal data, bluetooth low energy ("BLE") signal data, and near field communication ("NFC") signal data.
5. A method as claimed in any one of claims 1 to 3, further comprising: the wireless signal data is determined by the plurality of access point computing devices based on data received from the user computing devices via one or more wireless communication channels.
6. A method as claimed in any one of claims 1 to 3, wherein the one or more characteristics of the wireless signal data comprise phase vector correlation.
7. The method of any of claims 1-3, wherein the one or more characteristics of the wireless signal data comprise: a difference in consecutive received signal strength indicators, a standard deviation of said difference in consecutive received signal strength indicators, or a ratio between a number of available received signal strength indicators and a total number of said first time interval and said second time interval.
8. The method of any of claims 1-3, wherein classifying the user computing device as mobile further comprises applying the received wireless signal data to a model comprising one or more of: recurrent neural networks, random forests, support vector machines, and hidden markov models.
9. A computer readable storage device having computer executable program instructions embodied therein which, when executed by a computer, cause the computer to determine a location of a user computing device based on wireless signal attribute data and motion classification, the computer readable program instructions comprising:
For carrying out the following operations computer readable program instructions: receiving wireless signal data associated with a user computing device for a time window from a plurality of access point computing devices, the time window comprising at least a first time interval and one or more second time intervals preceding the first time interval;
for carrying out the following operations computer readable program instructions: extracting, for each of the plurality of access point computing devices, one or more features of the wireless signal data associated with the user computing device at the first time interval and at the one or more second time intervals preceding the first time interval;
For carrying out the following operations computer readable program instructions: determining an aggregate feature based on the extracted one or more features of the wireless signal data received from the plurality of access point computing devices;
For carrying out the following operations computer readable program instructions: classifying the user computing device as mobile based on the determined aggregated features, and
For carrying out the following operations computer readable program instructions: determining a location of the user computing device based on the received wireless signal attribute data in response to classifying the user computing device as mobile; and
For carrying out the following operations computer readable program instructions: the determined location of the user computing device is sent to a second computing device for display via the second computing device.
10. The computer-readable storage device of claim 9, wherein the one or more wireless signal data comprises one or more of: a received signal strength indicator and a phase vector.
11. The computer readable storage device of claim 10, further comprising computer readable program instructions for: an angle of arrival is determined based on the received phase vector, and wherein the location of the user computing device is determined based on the determined angle of arrival and the received signal strength indicator.
12. The computer readable storage device of any of claims 9 to 11, wherein the wireless signal data comprises one or more of: wi-Fi signal data, bluetooth low energy ("BLE") signal data, near field communication ("NFC") signal data.
13. The computer-readable storage device of any of claims 9 to 11, wherein the plurality of access point computing devices determine the wireless signal attribute data based on data received from the user computing device via one or more wireless communication channels.
14. The computer readable storage device of any of claims 9 to 11, wherein the one or more characteristics of the wireless signal data include phase vector correlation.
15. The computer-readable storage device of any of claims 9 to 11, wherein the one or more characteristics of the wireless signal data include: a difference in consecutive received signal strength indicators, a standard deviation of said difference in consecutive received signal strength indicators, or a ratio between a number of available received signal strength indicators and a total number of said first time interval and said second time interval.
16. A system for determining a location of a user computing device based on wireless signal attribute data and motion classification, configured to:
receiving wireless signal attribute data associated with a user computing device for a time window from a plurality of access point computing devices, the time window comprising at least a first time interval and one or more second time intervals preceding the first time interval;
Extracting, for each of the plurality of access point computing devices, one or more features of the wireless signal attribute data associated with the user computing device at the first time interval and at the one or more second time intervals preceding the first time interval;
determining an aggregate feature based on the extracted one or more features of the wireless signal attribute data received from the plurality of access point computing devices;
Classifying the user computing device as mobile based on the determined aggregated features, and
Responsive to classifying the user computing device as mobile, determining a location of the user computing device based on the received wireless signal attribute data; and
The determined location of the user computing device is sent to a second computing device.
17. The system of claim 16, wherein the one or more characteristics of the wireless signal attribute data include one or more of: a received signal strength indicator and a phase vector.
18. The system of any of claims 16 to 17, wherein the one or more characteristics of the wireless signal attribute data include one or more of: wi-Fi signal data, bluetooth low energy ("BLE") signal data, near field communication ("NFC") signal data.
19. The system of any of claims 16 to 17, further comprising the plurality of access point computing devices, wherein the plurality of access point computing devices are configured to determine the wireless signal attribute data based on data received from the user computing device via one or more wireless communication channels.
20. The system of any of claims 16 to 17, wherein classifying the user computing device as mobile further comprises applying the aggregated wireless signal characteristics to a model comprising one or more of: recurrent neural networks, random forests, support vector machines, and hidden markov models.
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