WO2017181952A1 - Positioning method and device - Google Patents

Abstract

A positioning method and device. The method comprises: establishing a finger database, the finger database comprising multiple fingerprints and each fingerprint comprising coordinate information and a received signal strength indication (RSSI) unit vector of a position point (110); obtaining RSSI measurement data, and obtaining an RSSI unit vector of a positioning point according to the RSSI measurement data (120); performing similarity matching between the RSSI unit vector of the positioning point and RSSI unit vectors of fingerprints in the fingerprint database, so as to obtain at least two matching fingerprints (130); and performing weighted average calculation on coordinate information of all the matching fingerprints, and using the calculation result as coordinate information of the positioning point (140).

Description

Positioning method and device Technical field

The present disclosure relates to the field of communication technologies, for example, to a positioning method and apparatus.

Background technique

The satellite positioning network represented by Global Positioning System (GPS) and Beidou has been able to achieve precise positioning outdoors. However, indoors, satellite positioning is not available in most cases due to weak satellite signals. With the increasing demand for indoor positioning, indoor mobile positioning will become the growth point of the next generation mobile network business.

In order to realize indoor mobile positioning, various technical solutions are proposed, such as gyroscope-based positioning, positioning based on signal arrival time measurement, and positioning based on Wireless Fidelity (wifi) signal strength.

Among them, based on the positioning of the gyroscope, there is a problem of accumulation of errors, which cannot be used for a long time. Positioning based on signal arrival time measurement requires multiple base stations to be strictly time synchronized, and requires high-accuracy measurement of the arrival time of the wireless signal, which is not supported by the relevant base station equipment. The positioning based on the wifi signal strength is a positioning method based on Received Signal Strength Indication (RSSI), which requires a dedicated wifi network to be deployed in the location area, which increases the additional cost.

Summary of the invention

The present disclosure provides a positioning method and apparatus, which can reduce system cost and improve positioning efficiency.

In a first aspect, a positioning method is proposed, comprising:

Establishing a fingerprint database, the fingerprint database includes a plurality of fingerprints, each fingerprint includes coordinate information of a location point and a received signal strength indication RSSI unit vector; acquiring RSSI measurement data, and obtaining an RSSI unit of the positioning point according to the RSSI measurement data vector;

Performing similarity matching on the RSSI unit vector of the positioning point and the RSSI unit vector of the fingerprint in the fingerprint database to obtain at least two matching fingerprints;

The weighted average calculation is performed on the coordinate information of all matching fingerprints, and the calculation result is used as the coordinate information of the positioning point.

Optionally, the fingerprint database includes cell information to which the location point corresponding to each fingerprint belongs, and similarity matching between the RSSI unit vector of the positioning point and the RSSI unit vector of the fingerprint in the fingerprint database. include:

Determining, by the target cell where the positioning point is located, selecting, according to the cell information corresponding to each fingerprint, a fingerprint set in the target cell from the fingerprint database;

A similarity matching is performed on the RSSI unit vector of the anchor point and the RSSI unit vector of the fingerprint in the selected fingerprint set.

Optionally, pre-establishing the fingerprint database includes:

Obtaining RSSI measurement data of a location point;

Obtaining an RSSI unit vector of the location point according to the RSSI measurement data of the location point;

Combining the coordinate information of the location point and the RSSI unit vector into one fingerprint and storing;

Store multiple fingerprints and create a fingerprint database.

Optionally, the RSSI unit vector of the positioning point is matched with the RSSI unit vector of the fingerprint in the fingerprint database to obtain at least two matching fingerprints, including:

Calculating a norm of the RSSI unit vector of the positioning point and the RSSI unit vector of the fingerprint in the fingerprint database, performing similarity matching according to the calculation result, and selecting the M fingerprints with the smallest calculation result as the matching fingerprint, M≥2.

Optionally, the weighted average calculation of the coordinate information of all matching fingerprints includes:

Perform weighted average calculation on the coordinate information of all matching fingerprints, and determine the weight according to the following formula:

Where w _k is the weight of the kth matching fingerprint, Q _k is a norm of the RSSI unit vector of the kth matching fingerprint and the RSSI unit vector of the anchor point, ε is a non-zero constant, / is a division operation symbol.

In a second aspect, a positioning device is provided, comprising:

Establishing a module, configured to establish a fingerprint database, the fingerprint database includes a plurality of fingerprints, each fingerprint includes coordinate information of a location point and a received signal strength indication RSSI unit vector;

a preprocessing module, configured to obtain RSSI measurement data, and obtain an RSSI unit vector of the positioning point according to the RSSI measurement data;

a matching module, configured to perform similarity matching on an RSSI unit vector of the positioning point and an RSSI unit vector of a fingerprint in the fingerprint database to obtain at least two matching fingerprints;

The positioning module is configured to perform weighted average calculation on coordinate information of all matching fingerprints, and use the calculation result as coordinate information of the positioning point.

Optionally, the fingerprint database includes cell information to which the location point corresponding to each fingerprint belongs, and the matching module is configured to:

Determining, by the target cell where the positioning point is located, selecting, according to the cell information corresponding to each fingerprint, a fingerprint set in the target cell from the fingerprint database;

A similarity matching is performed on the RSSI unit vector of the anchor point and the RSSI unit vector of the fingerprint in the selected fingerprint set.

Optionally, the establishing module is configured to: obtain RSSI measurement data of a location point; obtain an RSSI unit vector of the location point according to the RSSI measurement data of the location point; and coordinate information and RSSI units of the location point The vectors are combined into one fingerprint and stored; and multiple fingerprints are stored to create a fingerprint database.

Optionally, the matching module is configured to:

Calculating a norm of the RSSI unit vector of the positioning point and the RSSI unit vector of the fingerprint in the fingerprint database, performing similarity matching according to the calculation result, and selecting the M fingerprints with the smallest calculation result as the matching fingerprint, M≥2.

Optionally, the positioning module is configured to: determine a weight for performing a weighted average calculation according to the following formula:

Where w _k is the weight of the kth matching fingerprint, Q _k is a norm of the RSSI unit vector of the kth matching fingerprint and the RSSI unit vector of the anchor point, ε is a non-zero constant, / is a division operation symbol.

The present disclosure also provides a computer readable storage medium storing computer executable instructions arranged to perform the above method.

The present disclosure also provides an electronic device, the electronic device comprising:

At least one processor;

a memory communicatively coupled to the at least one processor; wherein

The memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to cause the at least one processor to perform the method described above.

The positioning method provided by the present disclosure establishes a database of RSSI unit vectors including location points, performs similarity matching on the RSSI unit vector of the fingerprint in the database and the RSSI unit vector of the anchor point in the positioning, and selects matching fingerprints from the database. The weighted average of the coordinate information of the matching fingerprint is used as the positioning result. The positioning solution process reduces the computational overhead, is conducive to large-scale calculations, reduces system cost, and improves positioning efficiency.

DRAWINGS

Figure 1 is a flow chart of a first embodiment of a positioning method;

2 is a flow chart of establishing a fingerprint database in the embodiment;

Figure 3 is a flow chart of a second embodiment of the positioning method;

4 is a schematic block diagram of an embodiment of a positioning device;

5 is a schematic structural diagram of a mobile network system applying an embodiment of a positioning method;

6 is a flowchart of positioning a user equipment by using an embodiment of a mobile network system application positioning method of FIG. 5;

7 is a signaling interaction diagram of each subject when the user equipment is located in the mobile network system application positioning method embodiment of FIG. 5;

FIG. 8 is a schematic structural diagram of hardware of an electronic device according to an embodiment.

Detailed ways

The embodiments described herein are merely illustrative of the disclosure and are not limiting of the disclosure. The technical features in the following embodiments and the embodiments may be arbitrarily combined with each other without conflict.

In the related art, based on the positioning of the wifi signal strength, a dedicated wifi network needs to be deployed in the location area. If the mobile network signal of the user equipment (User Equipment, UE) is used for positioning on the mobile network base station, the deployment cost of the wireless fidelity (wifi) network can be avoided. Therefore, using the mobile network to locate indoor and outdoor user equipment is a technical solution for realizing indoor mobile positioning.

When using the mobile network for positioning, since each user equipment has different transmission power, directly using the RSSI measurement data to perform fingerprint matching using the Euclidean distance, there is a problem that the fingerprint matching error is large. To this end, the related art is improved by the cosine similarity method. The formula for calculating the cosine similarity method is as follows:

Wherein the received signal strength indication measurement vector P _X is obtained (Received Signal Strength Indication, RSSI) measurements, P is _{R & lt} RSSI measurement data in the fingerprint database, the vector P is set n-dimensional vector, cosine similarity calculated once, It is necessary to do (n+1) multiplication, 2n square operation, 2 square root operations, 1 division operation, and 1 inverse cosine operation. The calculation amount is large, and the calculation cost is large in a large-scale positioning network.

Therefore, in the positioning method in the related art, since the Euclidean distance or the cosine similarity method is used for fingerprint matching, the algorithm has high complexity and large computational cost, which is not conducive to large-scale calculation, improves system cost, and reduces positioning efficiency. .

Referring to Figure 1, a flow chart of a first embodiment of a positioning method is presented.

In step 110, a fingerprint database is established, the fingerprint database including a plurality of fingerprints, each fingerprint containing coordinate information of a location point and an RSSI unit vector.

In this embodiment, a fingerprint database may be established in advance, and the fingerprint database includes a plurality of fingerprints, each fingerprint including coordinate information of a location point and an RSSI unit vector.

The process of creating a fingerprint database is shown in Figure 2.

In step 1110, RSSI measurement data for a location point is obtained.

The RSSI measurement data may be a signal power measurement vector, and the signal power measurement vector P of the position point coordinates (x, y) is an n-dimensional vector, P = [P ₁ , P ₂ , ..., P _n ].

Taking the positioning of the user equipment by using the mobile network as an example, when the mobile network is uplinked, P ₁ , P ₂ , ..., P _n are user equipments of the location points measured by n positioning signal measuring stations (User Equipment, UE) The power of the transmitted signal; and when the mobile network is downlink, P ₁ , P ₂ , ..., P _n are the power of the signals transmitted by the n base stations measured by the user equipment.

In step 1120, an RSSI unit vector of the location point is obtained based on the RSSI measurement data of the location point.

Find the normalized vector λ of the measurement vector P, λ=P/|P|=[λ ₁ , λ ₂ , . . . , λ _n ], and use λ as the RSSI unit vector of the position point (x, y), where λ ₁ , λ ₂ , ..., λ _n are components of the unit vector λ.

In step 1130, the coordinate information of the location point and the RSSI unit vector are combined into one fingerprint and stored.

Combine the coordinate information (x, y) of the position point (x, y) and the RSSI unit vector [λ ₁ , λ ₂ , ..., λ _n ] into one fingerprint, and define the fingerprint f = [x, y, λ ₁ , λ ₂ , . . . , λ _n ], and store the fingerprint f.

In step 1140, a plurality of fingerprints are stored to establish a fingerprint database.

Repeating the above steps 1110 to 1130, storing a plurality of fingerprints, and establishing a fingerprint database.

For example, a fingerprint data table as shown below can be stored as a fingerprint database:

Serial number	x	y	λ 1	λ 2	...	λ n
1
2
...
N

The established fingerprint database includes N (N ≥ 2) fingerprints, each fingerprint contains coordinate information (x, y) of one position point and RSSI unit vector [λ ₁ , λ ₂ , ..., λ _n ].

In step 120, the RSSI measurement data is acquired, and the RSSI unit vector of the positioning point is obtained according to the RSSI measurement data.

Once the fingerprint database is established, it can be located to obtain RSSI measurement data. The RSSI measurement data may be a signal power measurement vector P, let P = [P ₁ , P ₂ , ..., P _n ], and P be an n-dimensional vector.

Taking the positioning of the user equipment by using the mobile network as an example, when the mobile network is positioned in the uplink, P ₁ , P ₂ , . . . , P _n are signals transmitted by the user equipment of the location point measured by the n positioning signal measuring stations. Power; and when the mobile network is downlink-located, P ₁ , P ₂ , ..., P _n are the powers of signals transmitted by the n base stations measured by the user equipment.

Find the normalized vector λ of the measurement vector P, λ=P/|P|=[λ ₁ , λ ₂ ,...,λ _n ], and use λ as the RSSI of the anchor point (such as the location point where the user equipment is located) A unit vector, where λ ₁ , λ ₂ , ..., λ _n are components of the unit vector λ.

In step 130, the RSSI unit vector of the anchor point and the RSSI of the fingerprint in the fingerprint database The unit vector performs similarity matching to obtain at least two matching fingerprints.

In this step 130, each fingerprint in the fingerprint database is traversed, and the RSSI unit vector of the anchor point is similarly matched with the RSSI unit vector of each fingerprint in the fingerprint database to obtain at least two matching fingerprints.

Alternatively, similarity matching can be performed by calculating a norm of the RSSI unit vector. Optionally, calculating a norm Q of the RSSI unit vector of the anchor point and the RSSI unit vector of each fingerprint in the fingerprint database, the calculation formula may be:

_{Wherein, λ r = [λ r1,} λ r2, ..., λ rn], as fingerprint database RSSI unit vector of a _{fingerprint; λ x = [λ x1,} λ x2, ..., λ xn], is The RSSI unit vector of the anchor point, where n is the dimension of the measurement vector P.

The similarity matching is performed according to a norm calculation result Q. The smaller the Q value is, the closer the two vectors are, the more similar the two are, the M (M ≥ 2) fingerprints with the smallest calculation result Q are selected as matching fingerprints, and M can be selected as 3 or 4. For example, to obtain n calculation results Q1, Q2, Q3, Q4, ..., Qn from small to large, the three fingerprints corresponding to the calculation results Q1-Q3 may be selected as matching fingerprints, or four corresponding to Q1-Q4 may be selected. The fingerprints are matching fingerprints.

In step 140, weighted average calculation is performed on coordinate information of all matching fingerprints, and the calculation result is used as coordinate information of the positioning point.

Optionally, a weighted average calculation is performed according to the following formula:

Where W _k is the weight of the kth matching fingerprint, x _k is the x coordinate of the kth matching fingerprint, and y _k is the y coordinate of the kth matching fingerprint.

In the current step 130, when the similarity matching is performed by calculating a norm of the RSSI unit vector, the weighted value _{k k} for performing the weighted average calculation may be obtained according to the weighted nearest neighbor method, and the calculation formula is as follows:

Where Q _k is a norm of the RSSI unit vector of the kth matching fingerprint and the RSSI unit vector of the anchor point, and ε is a very small non-zero constant, for example, ε ≤ ^{10 -10} , which can avoid the denominator being 0 In the case, / is the division operator.

The weighted average calculation results xx and yy are used as positioning results, that is, (xx, yy) is used as the coordinates of the positioning point to realize positioning of the user equipment.

The positioning method of this embodiment establishes a database containing RSSI unit vectors of each location point, performs similarity matching on the RSSI unit vector of the fingerprint in the database and the RSSI unit vector of the anchor point in the positioning, and selects a matching fingerprint from the database. The weighted average of the coordinate information of the matching fingerprint is used as the positioning result. The positioning solution process reduces the computational overhead, is conducive to large-scale calculations, reduces system cost, and improves positioning efficiency.

Referring to Figure 3, a second embodiment of the positioning method is presented.

In step 210, a fingerprint database is established. The fingerprint database includes a plurality of fingerprints and cell information corresponding to each fingerprint, and each fingerprint includes coordinate information of a location point and an RSSI unit vector.

The process of establishing the fingerprint database in this step 210 can be the same as in step 110 in the first embodiment.

In the meantime, on the basis of the first embodiment, the fingerprint database of the embodiment adds the cell information to which the location point corresponding to the fingerprint belongs, such as a cell identifier (Identification, ID). For example, a fingerprint data table as shown below can be stored as a fingerprint database:

Serial number	Cell ID	x	y	λ 1	λ 2	...	λ n
1
2
...
n

The established fingerprint database includes N (N ≥ 2) fingerprints, each fingerprint contains coordinate information (x, y) of a position point and RSSI unit vectors [λ ₁ , λ ₂ , ..., λ _n ] and The ID of the cell to which the location point belongs.

In step 220, the RSSI measurement data is acquired, and the RSSI unit vector of the positioning point is obtained according to the RSSI measurement data.

This step 220 can be the same as step 120 in the first embodiment.

In step 230, the target cell where the positioning point is located is determined, and the fingerprint set in the target cell is selected from the fingerprint database according to the cell information corresponding to each fingerprint.

Optionally, the target cell is determined according to the RSSI measurement data or the base station determining the cell range where the positioning point is located. The fingerprint database is queried, and all the fingerprints in the target cell are selected from the fingerprint database according to the cell information corresponding to each fingerprint, and a fingerprint set is obtained, and the location points corresponding to all the fingerprints in the fingerprint set belong to the target cell.

In this embodiment, steps 220 and 230 may be performed simultaneously, or step 230 may be performed first and then step 220 may be performed.

In step 240, the RSSI unit vector of the anchor point is matched with the RSSI unit vector of the fingerprint in the selected fingerprint set to obtain at least two matching fingerprints.

In this step 240, instead of traversing each fingerprint in the entire database, each fingerprint in the selected fingerprint set may be traversed, and the RSSI unit vector of the anchor point and the RSSI unit vector of each fingerprint in the fingerprint set are similarized. Matching, at least two matching fingerprints are obtained, which reduces the amount of calculation and improves the positioning speed.

The manner in which the similarity matching is performed on the RSSI unit vector in this step 240 may be the same as the step 130 in the first embodiment.

In step 250, weighted average calculation is performed on coordinate information of all matching fingerprints, and the calculation result is used as coordinate information of the positioning point.

This step 250 can be the same as step 140 in the first embodiment.

Since the embodiment does not need to traverse each fingerprint in the entire database, only each fingerprint in the selected fingerprint set can be traversed, the calculation amount is reduced, and the positioning speed is improved.

Referring to FIG. 4, an embodiment of a positioning device is proposed. The device includes an establishing module 10, a preprocessing module 20, a matching module 30, and a positioning module 40.

The setup module 10 is configured to establish a fingerprint database. The fingerprint database includes a plurality of fingerprints, each of which includes coordinate information of a location point and a received signal strength indication RSSI unit vector.

The process of establishing the fingerprint database by the establishing module 10 may include: acquiring RSSI measurement data of a location point; obtaining an RSSI unit vector of the location point according to the RSSI measurement data of the location point; combining the coordinate information of the location point and the RSSI unit vector into One fingerprint and stored; and repeat the foregoing operation process, store multiple fingerprints, and establish a fingerprint database.

The pre-processing module 20 is configured to acquire RSSI measurement data, and obtain an RSSI unit vector of the positioning point according to the RSSI measurement data.

The pre-processing module 20 normalizes the acquired RSSI measurement data, obtains a normalized vector of the RSSI measurement data, and uses the obtained normalized vector as the RSSI unit vector of the positioning point.

The matching module 30 is configured to perform similarity matching on the RSSI unit vector of the positioning point and the RSSI unit vector of the fingerprint in the fingerprint database to obtain at least two matching fingerprints.

In some embodiments, the matching module 30 traverses each fingerprint in the fingerprint database, and similarly matches the RSSI unit vector of the anchor point with the RSSI unit vector of each fingerprint in the fingerprint database to obtain at least two matching fingerprints.

In some embodiments, when the fingerprint database is established, the establishing module 10 may also store the cell information to which the location point corresponding to each fingerprint belongs in the fingerprint database. At this time, the matching module 30 may determine the target cell of the positioning point according to the RSSI measurement data or the base station, select a fingerprint set in the target cell from the fingerprint database according to the cell information corresponding to the fingerprint, and traverse each fingerprint in the selected fingerprint set. The RSSI unit vector of the anchor point is similarly matched with the RSSI unit vector of each fingerprint in the fingerprint set, and at least two matching fingerprints are obtained, which reduces the calculation amount and improves the positioning speed.

Alternatively, the matching module 30 may perform similarity matching by calculating a norm of the RSSI unit vector. Optionally, the matching module 30 calculates a norm Q of the RSSI unit vector of the anchor point and the RSSI unit vector of each fingerprint in the fingerprint database, and the calculation formula may be:

_{Wherein, λ r = [λ r1,} λ r2, ..., λ rn], as fingerprint database RSSI unit vector of a _{fingerprint; λ x = [λ x1,} λ x2, ..., λ xn], is The RSSI unit vector of the anchor point, where n is the dimension of the measurement vector P.

The matching module 30 performs similarity matching according to a norm calculation result Q. The smaller the Q value is, the closer the two vectors are, the more similar the two are, the M (M ≥ 2) fingerprints with the smallest calculation result Q are selected as matching fingerprints, M Optional 3 or 4. For example, to obtain n calculation results Q1, Q2, Q3, Q4, ..., Qn from small to large, the three fingerprints corresponding to the calculation results Q1-Q3 may be selected as matching fingerprints, or four corresponding to Q1-Q4 may be selected. The fingerprints are matching fingerprints.

In addition, the matching module 30 can also perform similarity matching on the RSSI unit vector by using other similarity matching algorithms in the prior art.

The positioning module 40 is configured to perform weighted average calculation on coordinate information of all matching fingerprints, and use the calculation result as coordinate information of the positioning point.

Optionally, the positioning module 40 performs a weighted average calculation according to the following formula:

Where W _k is the weight of the kth matching fingerprint, x _k is the x coordinate of the kth matching fingerprint, and yk is the y coordinate of the kth matching fingerprint.

When the matching module 30 performs the similarity matching by calculating a norm of the RSSI unit vector, the positioning module 40 may obtain the weight w _k for performing the weighted average calculation according to the weighted neighbor method, and the calculation formula is as follows:

Where Q _k is a norm of the RSSI unit vector of the kth matching fingerprint and the RSSI unit vector of the anchor point, and ε is a very small non-zero constant, for example, ε ≤ ^{10 -10} , which can avoid the denominator being 0 In the case, / is the division operator.

The positioning module 40 uses the weighted average calculation results xx and yy as the positioning result, that is, (xx, yy) is used as the coordinates of the positioning point, and the positioning of the user equipment is implemented.

The positioning method of this embodiment establishes a database containing RSSI unit vectors of each location point, performs similarity matching on the RSSI unit vector of the fingerprint in the database and the RSSI unit vector of the anchor point in the positioning, and selects a matching fingerprint from the database. The weighted average of the coordinate information of the matching fingerprint is used as the positioning result. The positioning solution process reduces the computational overhead, is conducive to large-scale calculations, reduces system cost, and improves positioning efficiency.

The positioning method and apparatus of this embodiment can be applied to a plurality of scenarios based on receiving signal strength indications for positioning. For example, the positioning method and apparatus of the present embodiment can be applied to a mobile network system, and the user equipment is located by using a mobile network, which will be described in detail below with reference to the accompanying drawings.

As shown in FIG. 5, it is a schematic structural diagram of a mobile network system. The mobile network system includes a user equipment (UE), a network node (Mobility Management Entity, MME), an evolved base station (Evolved Node B, eNB), a radio remote unit (RRU), and a positioning signal measurement station (Location Measurement). Unit, LMU) and Evolved Serving Mobile Location Center (E-SMLC). The UE, the MME, the eNB, and the RRU are existing devices in the mobile network system, and the LMU and the E-SMLC are devices that are added to achieve positioning.

The LMU is a virtual device and is physically composed of an RRU and a Building Base Band Unit (BBU). Each LMU may include a baseband media access control layer (Media Access Control, MAC), physical layer (Physical, PHY) and RRU antenna. The position coordinates of the LMU are determined by the antenna position coordinates. The LMU can measure the power of the Sounding Reference Signal (SRS) signal transmitted by the UE (that is, the RSSI measurement data) and report it to the E-SMLC.

The E-SMLC may be the positioning device of the present embodiment, or the E-SMLC may include the positioning device of the present embodiment. The functions of the E-SMLC may include: establishing and maintaining a fingerprint database, receiving RSSI measurement data, and using the foregoing positioning method to determine the location coordinates of the UE.

The process of uplink positioning of the mobile network is shown in FIG. 6 and FIG. 7.

In step 100, the MME sends a Location Request message (Location Request) to the E-SMLC. The message carries parameters such as the ID of the target UE to be located, the number of times of positioning, and the measurement time interval.

In step 200, after receiving the location request message, the E-SMLC sends a Measurement Request message to the eNB.

In step 300, after receiving the measurement request message, the eNB acquires the RSSI measurement data through multiple LMUs and reports the data to the E-SMLC.

Optionally, the eNB determines the link entity of the target UE and all the LMUs in the cell of the target UE, allocates SRS resources, configures a micro RRU (PicoRRU, pRRU) that covers the local cell, prepares to receive the measured SRS signal, and adopts air interface signaling. The interface notifies the target UE to the SRS configuration, and adjusts the target UE power so that multiple pRRUs can receive.

The target UE transmits the SRS signal according to the specified parameter, and the plurality of LMUs receive the SRS signal of the target UE and measure the power of the SRS signal, that is, the RSSI measurement data.

Optionally, each LMU sends the separately obtained RSSI measurement data to the E-SMLC.

Optionally, the eNB aggregates the RSSI measurement data of the LMU and reports the measurement result to the E-SMLC by using a Measurement Response message.

After reporting the RSSI measurement data, the eNB restores the transmit power of the target UE.

In step 400, the E-SMLC receives the RSSI measurement data, and calculates the location coordinates of the target UE based on the RSSI measurement data and the fingerprint database.

In this step 400, the E-SMLC calculates the position coordinates of the target UE by using the positioning method of this embodiment. For the calculation procedure, refer to the first embodiment and the second embodiment of the foregoing method.

In step 500, the E-SMLC transmits the location coordinates of the target UE to the MME.

After calculating the location coordinates of the target UE, the E-SMLC sends the location coordinates of the target UE to the MME through a Location Response message, thereby realizing the positioning of the target UE.

Since wireless channels are generally symmetrical. Therefore, the positioning method of this embodiment is also applicable to the downlink positioning of the mobile network.

In implementation, when the fingerprint database is not placed on the UE side, the location solution can be performed on the E-SMLC side. The power of the signal transmitted by the multiple base stations (eNBs), that is, the RSSI measurement data, is measured on the UE side, and the RSSI measurement data is sent to the E-SMLC through the signaling or user data channel, and the E-SMLC is calculated by using the positioning method in this embodiment. The location coordinates of the UE are obtained to implement positioning of the UE.

If the UE side stores the fingerprint database, the UE may also use the positioning method based on the received signal strength indication in this embodiment to calculate the location coordinates of the UE. At this time, the UE may be the positioning device of this embodiment, or the UE may include the positioning device of this embodiment.

The positioning method of the embodiment is used to locate the user equipment in the mobile network system, which simplifies the positioning and solving process, reduces the calculation overhead, is beneficial to large-scale calculation, reduces system cost, and improves positioning efficiency.

The present embodiment provides a computer readable storage medium storing computer executable instructions arranged to perform the method of any of the above embodiments.

This embodiment provides a hardware structure diagram of an electronic device. Referring to FIG. 8, the electronic device includes:

At least one processor 80, which is exemplified by a processor 80 in FIG. 8; and a memory 81, may further include a communication interface 82 and a bus 83. Among them, the processor 80, the memory 81, and the communication interface 82 can complete communication with each other through the bus 83. Communication interface 82 can be configured for information transfer. Processor 80 can invoke logic instructions in memory 81 to perform the methods of the above-described embodiments.

In addition, the logic instructions in the memory 81 described above may be implemented in the form of a software functional unit and sold or used as a stand-alone product, and may be stored in a computer readable storage medium.

The memory 81 is a computer readable storage medium, and can be configured to store a software program, a computer executable program, a program instruction or a module corresponding to the method in the embodiment. The processor 80 executes the functional application and the data processing by executing a software program, an instruction or a module stored in the memory 81, that is, implementing the method in the above method embodiment.

The memory 81 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application required for at least one function; the storage data area may store data created according to usage of the device, and the like. Further, the memory 81 may include a high speed random access memory, and may also include a nonvolatile memory. Through the description of the foregoing implementation manners, the foregoing embodiment method may be implemented by means of software plus a general hardware platform, or may be implemented by hardware. The technical solution of the above embodiment can be embodied in the form of a software product stored in a storage medium (such as a read-only memory (ROM), a random access memory (RAM). , a disk, an optical disk, including one or more instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method described in the above embodiments.

Industrial applicability

The positioning method and device provided by the disclosure simplify the positioning solution process, reduce the calculation overhead, facilitate large-scale calculation, reduce system cost, and improve positioning efficiency.

Claims (11)

1. A positioning method comprising:

   Establishing a fingerprint database, the fingerprint database includes a plurality of fingerprints, each fingerprint includes coordinate information of a location point and a received signal strength indication RSSI unit vector; acquiring RSSI measurement data, and obtaining an RSSI unit of the positioning point according to the RSSI measurement data vector;

   Performing similarity matching on the RSSI unit vector of the positioning point and the RSSI unit vector of the fingerprint in the fingerprint database to obtain at least two matching fingerprints;

   The weighted average calculation is performed on the coordinate information of all matching fingerprints, and the calculation result is used as the coordinate information of the positioning point.
2. The method according to claim 1, wherein the fingerprint database includes cell information to which a location point corresponding to each fingerprint belongs, and the RSSI unit vector of the anchor point and a fingerprint in the fingerprint database. The similarity matching of the RSSI unit vector includes:

   Determining, by the target cell where the positioning point is located, selecting, according to the cell information corresponding to each fingerprint, a fingerprint set in the target cell from the fingerprint database;

   A similarity matching is performed on the RSSI unit vector of the anchor point and the RSSI unit vector of the fingerprint in the selected fingerprint set.
3. The method of claim 1 wherein pre-establishing the fingerprint database comprises:

   Obtaining RSSI measurement data of a location point;

   Obtaining an RSSI unit vector of the location point according to the RSSI measurement data of the location point;

   Combining the coordinate information of the location point and the RSSI unit vector into one fingerprint and storing;

   Store multiple fingerprints and create a fingerprint database.
4. The method according to any one of claims 1-3, wherein the RSSI unit vector of the anchor point is similarly matched with the RSSI unit vector of the fingerprint in the fingerprint database to obtain at least two matching fingerprints, including :

   Calculating a norm of the RSSI unit vector of the positioning point and the RSSI unit vector of the fingerprint in the fingerprint database, performing similarity matching according to the calculation result, and selecting the M fingerprints with the smallest calculation result as the matching fingerprint, M≥2.
5. The method of claim 4 wherein said performing a weighted average calculation of coordinate information for all matching fingerprints comprises:

   Perform weighted average calculation on the coordinate information of all matching fingerprints, and determine the weight according to the following formula:

   

   Where w _k is the weight of the kth matching fingerprint, Q _k is a norm of the RSSI unit vector of the kth matching fingerprint and the RSSI unit vector of the anchor point, ε is a non-zero constant, / is a division operation symbol.
6. A positioning device comprising:

   Establishing a module, configured to establish a fingerprint database, the fingerprint database includes a plurality of fingerprints, each fingerprint includes coordinate information of a location point and a received signal strength indication RSSI unit vector;

   a preprocessing module, configured to obtain RSSI measurement data, and obtain an RSSI unit vector of the positioning point according to the RSSI measurement data;

   a matching module, configured to perform similarity matching on an RSSI unit vector of the positioning point and an RSSI unit vector of a fingerprint in the fingerprint database to obtain at least two matching fingerprints;

   The positioning module is configured to perform weighted average calculation on coordinate information of all matching fingerprints, and use the calculation result as coordinate information of the positioning point.
7. The device according to claim 6, wherein the fingerprint database includes cell information to which a location point corresponding to each fingerprint belongs, and the matching module is configured to:

   Determining, by the target cell where the positioning point is located, selecting, according to the cell information corresponding to each fingerprint, a fingerprint set in the target cell from the fingerprint database;

   The RSSI unit vector of the anchor point and the RSSI unit direction of the fingerprint in the selected fingerprint set The quantity is similarly matched.
8. The apparatus according to claim 6, wherein the establishing module is configured to: acquire RSSI measurement data of a location point; obtain an RSSI unit vector of the location point according to RSSI measurement data of the location point; The coordinate information of the point and the RSSI unit vector are combined into one fingerprint and stored; and a plurality of fingerprints are stored to establish a fingerprint database.
9. The apparatus of any of claims 6-8, wherein the matching module is configured to:

   Calculating a norm of the RSSI unit vector of the positioning point and the RSSI unit vector of the fingerprint in the fingerprint database, performing similarity matching according to the calculation result, and selecting the M fingerprints with the smallest calculation result as the matching fingerprint, M≥2.
10. The apparatus of claim 9, wherein the positioning module is configured to determine a weight for performing a weighted average calculation according to the following formula:

    

    Where w _k is the weight of the kth matching fingerprint, Q _k is a norm of the RSSI unit vector of the kth matching fingerprint and the RSSI unit vector of the anchor point, ε is a non-zero constant, / is a division operation symbol.
11. A computer readable storage medium storing computer executable instructions arranged to perform the method of any of claims 1-5.

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