CN109640251B - Indoor positioning method and device - Google Patents
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Abstract
The invention discloses an indoor positioning method, which comprises the following steps: determining an effective indoor positioning area according to indoor space distribution; according to the internal structure of the structural body of the effective indoor positioning area, a sampling marking position is set in a self-adaptive mode, a corresponding radio signal transmitting device is deployed, and sampling data are obtained; the positioning points are locked through the self-adaptive sampling point network communication and topological structure; through the radio signal data of the indoor space of real-time collection, combine with a plurality of sensor data at smart machine terminal, mix the location, it is big to have solved the construction degree of difficulty among the current indoor positioning technology, and the computational efficiency is low, the problem of space positioning deviation.
Description
Technical Field
The application relates to the field of positioning, in particular to an indoor positioning method and an indoor positioning device.
Background
Location Based service (lbs) is an incremental service provided by using location information or motion trajectory information of a user, and has been widely applied to a plurality of fields such as intelligent medical treatment, security protection, commercial advertisement, social application, intelligent travel, map navigation, etc., and plays an important role in current society with higher and higher informatization degree.
Since the advent of positioning technologies represented by GPS, the characteristics of high efficiency, convenience, rapidness, and accuracy have caused a great change in people's lives, driving the rapid development of a range of applications and services. However, due to the limitation of the GPS technology, the technology can only be used for positioning outdoors, and cannot solve the positioning problem of indoor environment. As technologies such as smart cities, internet of things and mobile internet become high points of a new round of competition in the global information industry, and as a common basis of the fields, "location services" is particularly important, and is deeply embedded into the fields of national economy, the demand of people for indoor positioning is increasing day by day, and how to solve the problem of indoor positioning by technical means to meet the actual precision demand becomes a problem to be solved urgently.
However, most of the existing technologies or products focus on balancing the cost and the construction difficulty, and improving the quality of radio signals and the positioning accuracy. In a specific scene, especially in the aspect of an indoor positioning technology facing an intelligent mobile terminal, the pursuit of positioning accuracy cannot meet the use requirements of users, for example, in the aspect of positioning specific scenes of a market, a hospital and an exhibition, the positioning accuracy cannot be measured by absolute errors, and the use habits of the users and actual scene maps are combined to perform high-accuracy scene-based positioning (from a market to a counter, from a shop, from a hospital to a department, from the exhibition to an exhibition stand), which is specifically represented as follows:
1) the construction difficulty is large, the period is long: in the existing indoor positioning technology, the two modes of fingerprint positioning based on WiFi AP and positioning based on Bluetooth are mainly adopted. Both of the two main indoor positioning methods, i.e. equipment deployment, hardware procurement cost and construction period, generate high cost and consume a lot of time and labor.
2) The calculation efficiency is low: with the increase of the indoor positioning scene area, a large number of sampling positions need to be added, so that a large number of operation logics are generated in the matching operation process, and the positioning calculation efficiency is reduced.
3) Spatial positioning deviation: most of the traditional indoor positioning evaluation methods use positioning accuracy and errors as standards, that is, indoor space distribution and pattern are not considered, and such evaluation methods are one-sided and lack understanding of maps and scenes.
Disclosure of Invention
The application provides an indoor positioning method, which solves the problems of high construction difficulty, low calculation efficiency and space positioning deviation in the existing indoor positioning technology.
The application provides an indoor positioning method, which is characterized by comprising the following steps:
determining an effective indoor positioning area according to indoor space distribution;
according to the internal structure of the structural body of the effective indoor positioning area, a sampling marking position is set in a self-adaptive mode, a corresponding radio signal transmitting device is deployed, and sampling data are obtained; the positioning points are locked through the self-adaptive sampling point network communication and topological structure;
the radio signal data of the indoor space collected in real time is combined with the data of a plurality of sensors of the intelligent equipment terminal to carry out mixed positioning.
Preferably, the determining an effective indoor positioning area according to the indoor spatial distribution includes:
determining an effective positioning area on the basis of an indoor two-dimensional plane according to indoor space distribution;
and defining the closed range of the two-dimensional space of each effective positioning area and the positioning precision requirement.
Preferably, the method further comprises the following steps:
the positioning area is divided into a static scene and a dynamic scene according to a usage scene.
Preferably, the static scene includes:
and dividing the static scene into a small-area closed positioning scene, a large-area closed positioning scene and a wall-free open positioning scene according to the area and the division rule of the positioning scene.
Preferably, the dynamic scene specifically includes: and dynamically navigating and positioning the scene.
Preferably, said deploying the respective radio signal transmitting apparatus comprises:
setting a maximum value d, wherein d is the maximum interval distance of sampling points in a unit range;
WiFi APs are deployed at the sampling point.
Preferably, the method further comprises the following steps:
on the basis that the WiFi AP is deployed at the sampling point, the low-power Bluetooth can be deployed, and the positioning precision and reliability are enhanced.
Preferably, the network connection and topology structure through the adaptive sampling point, and the locking of the positioning point include:
the positioning points are locked in a specified range through self-adaptive sampling point network communication and a topological structure;
and locking the positioning points by iterative fitting of the sampling data with the specified frequency.
Preferably, the radio signal data of the indoor space collected in real time is combined with the data of a plurality of sensors of the intelligent device terminal to perform hybrid positioning, and the hybrid positioning method includes:
when the intelligent equipment terminal is used for starting navigation or path planning to carry out motion positioning, setting a threshold value lamda by combining indoor map road network data;
when the deviation value between the actual positioning position and the road network is greater than the threshold value lamda, starting a static positioning mode;
and taking the static positioning position point as an initial point to continue navigation until the path guidance is finished or the navigation is artificially terminated.
This application provides an indoor positioner simultaneously, its characterized in that includes:
a positioning area determining unit for determining an effective indoor positioning area according to indoor spatial distribution;
the sampling data acquisition unit is used for adaptively setting sampling marking positions according to the internal structure of the structural body of the effective indoor positioning area, deploying corresponding radio signal transmitting devices and acquiring sampling data; the positioning points are locked through the self-adaptive sampling point network communication and topological structure;
and the positioning unit is used for performing mixed positioning by combining the radio signal data of the indoor space acquired in real time with the data of the plurality of sensors of the intelligent equipment terminal.
According to the indoor positioning method, the effective positioning area is determined through indoor space distribution, the WiFi AP and the low-power-consumption Bluetooth are deployed in the effective positioning area, then the sampling marking position is set in a self-adaptive mode, and mixed positioning is carried out on indoor space radio signal data acquired in real time and multi-sensor data of an intelligent device terminal, so that the problems of high construction difficulty, low calculation efficiency and space positioning deviation in the existing indoor positioning technology are solved.
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Fig. 1 is a schematic flow chart of an indoor positioning method according to an embodiment of the present disclosure;
FIG. 2 is a schematic diagram of an adaptive setting sampling annotation position according to an embodiment of the present application;
fig. 3 is a schematic view of an indoor positioning apparatus according to an embodiment of the present disclosure.
Detailed Description
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. This application is capable of implementation in many different ways than those herein set forth and of similar import by those skilled in the art without departing from the spirit of this application and is therefore not limited to the specific implementations disclosed below.
Referring to fig. 1, fig. 1 is a schematic flow chart of an indoor positioning method according to an embodiment of the present application, and the method according to the embodiment of the present application is described in detail below with reference to fig. 1.
And step S101, determining an effective indoor positioning area according to indoor space distribution.
According to indoor space distribution, an effective positioning area is determined on the basis of an indoor two-dimensional plane, namely self-adaptive gridding is carried out, and corresponding positioning precision requirements are given to all the areas. The step is an initial non-digital implementation stage, manual intervention is needed, and the two-dimensional space closed range and the positioning precision requirement of each effective positioning area are clearly defined. Meanwhile, the road network data is digitized, namely represented by the mutual connection form of points and lines, so as to be used for subsequent dynamic positioning.
The method comprises the steps of dividing a positioning area into a static scene and a dynamic scene according to a use scene, abandoning the pursuit of one party to high positioning precision, subdividing an indoor positioning scene into two scenes by concepts such as reasonable control cost and efficient information acquisition, and then dividing the static scene into a small-area closed positioning scene, a large-area closed positioning scene and a wall-free open positioning scene according to the area and the dividing rule of the positioning scene. The dynamic field mainly refers to a dynamic navigation positioning scene.
The following is a basic description taking actual scenes such as large shopping malls and hospitals as examples.
A first field: static scenes
The static scene is mainly used for calculating the initial position of a user (a consumer in a market, a patient in a hospital and the like) and determining the position of the user or equipment; or when the user deviates from the preset route of the system in the process of guiding by using the path planning, the position calculation of the position correction is carried out. At this time, the user is in a relatively static state and has no destination planning, so that the user is a static scene, and the method is specifically classified into 3 types:
1) small-area closed positioning scene
According to a specific indoor space distribution structure and user precision requirements, the area of a smaller closed scene can be defined by user, such as a smaller consulting room in a hospital, a brand shop with a smaller area in a market and the like. In the positioning of such a scene, the positioning method provided by the technology focuses more on whether the actual positioning deviation distance is used for measuring the accuracy in the region.
2) Large area enclosed positioning scenario
The non-open scene outside the small-area closed area is defined as a larger closed positioning scene, such as a waiting room and an infusion room with larger area in a hospital or a shop with larger area or a partition in a market. By a recursive method, the scenes are gradually focused to a computable level through a two-dimensional space structure, and further closed scene positioning in a large area is realized.
3) Wall-free open positioning scene
In the environment without wall or closed structure, the system is defined as an open positioning scene, such as an open hall, a coffee hall, a special cosmetic cabinet in a market, a registration queuing area in a hospital, charge after treatment, a medicine taking point and the like. Although the influence of indoor two-dimensional space distribution is small, in the practical use of a user, the change of people flow or density is large, and the acquired space wireless signals need to be processed and then calculated, so that the positioning error caused by noise is avoided.
The second type of scene: dynamic class scenes
The dynamic scene mainly comprises searching and inquiring a target position, and carrying out real-time dynamic positioning along with the movement of a user when navigation is carried out through path planning provided by the system.
4) Dynamic navigation positioning scene
After the initial position and the target position are specified, the system can calculate and recommend a route according to a map structure, and dynamic positioning needs to be carried out on the user equipment in real time in the real-time piloting process. The method comprises the steps of calculating various attitude sensing data acquired by equipment through an inertial navigation technology, and correcting positions through methods such as road network binding and matching in the calculation process. Meanwhile, whether the equipment deviates from a preset route or not is judged in real time, and after the deviation is confirmed, the positioning and path planning are carried out again through a preorder method.
Step S102, according to the internal structure of the structural body of the effective indoor positioning area, a sampling marking position is set in a self-adaptive mode, a corresponding radio signal transmitting device is deployed, and sampling data are obtained; and locking the positioning points through the network communication and the topological structure of the self-adaptive sampling points.
According to the internal structure of the structure body of the effective indoor positioning area and corresponding positioning precision requirements of each area, a sampling marking position is set in a self-adaptive mode, then a maximum value d is set, d is the maximum interval distance of sampling points in a unit range, and the effective empirical value is generally 20 meters; then, a WiFi AP is deployed at a sampling point, and for an area with higher precision requirement, a low-power-consumption Bluetooth (BLE) signal transmitting terminal can be deployed on the basis of coverage of the WiFi AP, so that the positioning precision and reliability are enhanced; and (2) marking sampling points at the break points in the closed range of each effective positioning range clearly divided in the step (1), and acquiring n groups of space radio signals at the sampling points through intelligent terminal equipment, wherein the effective experience value is generally 300 and the acquisition interval is 500 milliseconds.
In order to meet the spatial constraint of scene subdivision, under the conditions of complex indoor structure and uneven distribution, the accurate positioning requirement of a specific scene cannot be met no matter the response time and the accuracy are achieved by utilizing the existing large-scale fingerprint matching algorithm.
Therefore, the application provides a grid-based self-adaptive sampling position labeling method on the basis of the thought of R tree spatial distribution. In the two-dimensional plane map capable of being marked, sampling marking positions are set in a self-adaptive mode according to the internal structure of the existing structural body, and the sampling marking positions are shown in figure 2. The positioning points are accurately locked in a specific range through self-adaptive sampling point network communication and a topological structure, and the actual position is approximated through iterative fitting of sampling data with specific frequency.
Meanwhile, under the scene that the density of part of POI (points of interest) is higher or the precision requirement is stronger, the positioning precision is improved through the requirement analysis with higher discrimination and a small amount of laying BLE signal emission sources, and further the functional requirement of a user under the scene is improved.
And S103, combining the radio signal data of the indoor space acquired in real time with the data of the plurality of sensors of the intelligent equipment terminal to perform mixed positioning.
The radio signal data of the indoor space collected in real time is combined with the data of a plurality of sensors of the intelligent equipment terminal to carry out mixed positioning. When a user uses the intelligent equipment terminal to start navigation or path planning to carry out motion positioning, a threshold value lambda is set by combining road network data of an indoor map, when the deviation value of the actual positioning position and the road network is greater than the threshold value lambda, a static positioning mode is started, and the static positioning position point is used as an initial point to continue navigation until path guidance is finished or the navigation is terminated artificially.
The invention can realize high-precision indoor positioning facing scene subdivision, and the main processing flow is as follows:
a sample collection step:
in the aspect of initial position calculation, the invention adopts a positioning method based on radio fingerprint characteristics, wherein the radio comprises infrastructures such as WLAN, locally deployed BLE and the like. The effective distance of WLAN signals transmitted in an open area is long, the effective signal searching and identifying distance of the WLAN signals in an indoor environment can reach more than 50m, the WLAN network has strong expandability, can be conveniently integrated with the existing networks, such as a wired Ethernet and the like, and is simple in networking and low in cost.
Meanwhile, the WLAN communication technology adopts an FDM modulation mode, and has better spectrum utilization rate and strong multipath effect resistance. Most public scenes such as superstores, stations, airports, hospitals and the like cover the WLAN space environment of the ad hoc network, so that a WLAN-based grid positioning method is selected under the consideration of various aspects such as cost, construction amount and the like, and a BLE mixed mode is selected in an area with low signal coverage density.
Because the space especially indoor radio signal's jump characteristics, the sampling positioning signal that this application designed filters the step:
and updating the estimation value of the state variable through the estimation value of the previous moment and the observation value of the current moment, and calculating the estimation value of the current moment. The essence of the method is that the observation value is used for continuously correcting the state vector of the system, recursion is carried out through the sequence of 'actual measurement-prediction-correction', the observation value of the system is used for eliminating random interference, and the state of the system is reproduced.
The filtering algorithm recursive logic sub-steps are as follows:
1. calculating single step prediction quantity of the current state;
2. calculating a single-step prediction matrix of error covariance;
3. calculating a gain;
4. correcting the state estimate according to the current observation;
5. the error covariance matrix is updated.
The radio fingerprint feature positioning method is mainly used for initial positioning or position determination after the intelligent mobile terminal is in an intermittent sleep mode. The overall work flow substep of the indoor positioning technology for initial position calculation in the application is as follows:
1. extracting the characteristics of sampling points;
2. carrying out probability statistics on the sampled data;
3. calculating the test points and the sampling characteristics;
4. selecting nearest neighbor sampling points;
5. selecting a nearest neighbor grid;
6. calculating a weighted position;
7. and outputting a positioning result.
The present application also provides an indoor positioning apparatus 300, please refer to fig. 3, which is characterized in that the apparatus includes:
a positioning area determination unit 310 for determining an effective indoor positioning area according to the indoor spatial distribution;
a sampling data acquisition unit 320 for deploying corresponding radio signal transmitting devices in the effective indoor positioning area; according to the internal structure of the structural body of the effective indoor positioning area, a sampling marking position is set in a self-adaptive mode, and sampling data are obtained;
the positioning unit 330 is used for locking positioning points through the network communication and topological structure of the adaptive sampling points; the radio signal data of the indoor space collected in real time is combined with the data of a plurality of sensors of the intelligent equipment terminal to carry out mixed positioning.
According to the method, the effective positioning area is determined through indoor space distribution, the WiFi AP and the low-power-consumption Bluetooth are deployed in the effective positioning area, then the sampling marking position is set in a self-adaptive mode, and mixed positioning is carried out on indoor space radio signal data acquired in real time and multi-sensor data of the intelligent device terminal, so that the problems of high construction difficulty, low calculation efficiency and space positioning deviation in the existing indoor positioning technology are solved.
Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art can make modifications and equivalents to the embodiments of the present invention without departing from the spirit and scope of the present invention, which is set forth in the claims of the present application.
Claims (9)
1. An indoor positioning method, comprising:
determining an effective indoor positioning area according to indoor space distribution;
according to the internal structure of the structural body of the effective indoor positioning area, the positioning device sets a sampling marking position in a self-adaptive manner, and deploys a corresponding radio signal transmitting device, wherein the radio signal transmitting device acquires sampling data; the positioning device is communicated with the topological structure through a self-adaptive sampling point network to lock positioning points;
the radio signal data of the indoor space that positioner was through real-time collection combines with a plurality of sensor data at smart machine terminal, mixes the location, includes: when the intelligent equipment terminal is used for starting navigation or path planning to carry out motion positioning, setting a threshold value lamda by combining indoor map road network data; when the deviation value between the actual positioning position and the road network is greater than the threshold value lamda, the positioning device starts a static positioning mode; and the intelligent equipment terminal takes the static positioning position point as an initial point to continue navigation until the path guidance is finished or the navigation is terminated artificially.
2. The method of claim 1, wherein determining an effective indoor positioning region from the indoor spatial distribution comprises:
determining an effective positioning area on the basis of an indoor two-dimensional plane according to indoor space distribution;
and defining the closed range of the two-dimensional space of each effective positioning area and the positioning precision requirement.
3. The method of claim 1 or 2, further comprising:
the positioning area is divided into a static scene and a dynamic scene according to a usage scene.
4. The method of claim 3, the static scenario, comprising:
and dividing the static scene into a small-area closed positioning scene and/or a large-area closed positioning scene and/or a wall-free open positioning scene according to the area and the division rule of the positioning scene.
5. The method according to claim 3, wherein the dynamic scenario specifically comprises: and dynamically navigating and positioning the scene.
6. The method of claim 1, wherein said deploying respective radio signal transmitting devices comprises:
setting a maximum valued，dThe maximum distance between sampling points in a unit range;
WiFi APs are deployed at the sampling point.
7. The method of claim 6, further comprising:
and deploying the low-power-consumption Bluetooth on the basis of deploying the WiFi AP at the sampling point.
8. The method of claim 1, wherein the positioning device locks the positioning points by connecting the adaptive sampling point network with the topology, comprising:
the positioning device is communicated with the topological structure through a self-adaptive sampling point network to lock the positioning point in a specified range;
and locking the positioning points by iterative fitting of the sampling data with the specified frequency.
9. An indoor positioning device, comprising:
a positioning area determining unit for determining an effective indoor positioning area according to indoor spatial distribution;
the sampling data acquisition unit is used for adaptively setting sampling marking positions by the positioning device according to the internal structure of the structural body of the effective indoor positioning area, deploying the corresponding radio signal transmitting devices and acquiring sampling data by the radio signal transmitting devices; the positioning device is communicated with the topological structure through a self-adaptive sampling point network to lock positioning points;
the positioning unit is used for the radio signal data of the indoor space of the positioning device through real-time acquisition and is combined with a plurality of sensor data of the intelligent equipment terminal to carry out mixed positioning, and the positioning unit comprises: when the intelligent equipment terminal is used for starting navigation or path planning to carry out motion positioning, setting a threshold value lamda by combining indoor map road network data; when the deviation value between the actual positioning position and the road network is greater than the threshold value lamda, the positioning device starts a static positioning mode; and the intelligent equipment terminal takes the static positioning position point as an initial point to continue navigation until the path guidance is finished or the navigation is terminated artificially.
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| CN111447549B (en) * | 2019-12-31 | 2021-06-15 | 华东理工大学 | Non-uniform UWB positioning error set network construction method and positioning error modeling method |
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