CN117177171A

CN117177171A - Positioning method, positioning device, electronic equipment and computer readable storage medium

Info

Publication number: CN117177171A
Application number: CN202311113933.7A
Authority: CN
Inventors: 邓中亮; 刘雯; 任海龙
Original assignee: Beijing University of Posts and Telecommunications
Current assignee: Beijing University of Posts and Telecommunications
Priority date: 2023-08-31
Filing date: 2023-08-31
Publication date: 2023-12-05

Abstract

The embodiment of the invention provides a positioning method, a positioning device, electronic equipment and a computer readable storage medium, wherein the positioning method comprises the following steps: acquiring RSRP and TOA when equipment to be positioned receives signals sent by a plurality of base stations at the current moment, inputting the RSRP into a cyclic neural network obtained by training in advance to obtain a first predicted position of the equipment to be positioned at the current moment, and determining Tx-Rx delay errors of all the base stations at the current moment according to the first predicted position of the equipment to be positioned at the current moment and the TOA; performing error elimination on TOAs according to Tx-Rx delay errors of each base station at the current moment to obtain TOAs after error elimination of each base station at the current moment; and determining the position coordinates of the equipment to be positioned at the current moment based on the TOA of each base station at the current moment after the errors are eliminated. On the premise of not increasing the reference terminal, the influence of Tx-Rx delay errors on the positioning result is reduced, and the positioning precision is further improved.

Description

Positioning method, positioning device, electronic equipment and computer readable storage medium

Technical Field

The present invention relates to the field of positioning technologies, and in particular, to a positioning method, a positioning device, an electronic device, and a computer readable storage medium.

Background

With the continuous progress of information technology, mobile communication technology is also continuously developing. 5G, a representative of a new generation of mobile communication technology, has begun to be commercially available worldwide. In addition to providing faster data transfer speeds and more reliable connections, 5G has many other important applications, for example in positioning technology, where positioning systems can more accurately perform positioning via 5G networks. Currently, a common positioning method is a positioning method based on a geometric relationship, for example, positioning solution is performed by using TOA (Time of Arrival), AOA (Angle of Arrival), and AOD (Angle of Depature, departure Angle) between a base station and a device to be positioned, so as to determine a position of the device to be positioned.

However, in the process of generating a digital signal in the baseband and transmitting/receiving a radio frequency signal from/to an antenna, a transmission-reception (Tx-Rx) delay error of hundred nanoseconds is generated, which causes a positioning error. In order to reduce the positioning error, a reference terminal is usually set before positioning, and then the Tx-Rx delay error is determined based on the reference terminal and the base station, so that the base station is calibrated, and the Tx-Rx delay error is eliminated.

Although this method can effectively eliminate Tx-Rx delay error, since the cost of arranging network hardware will be increased due to the need of adding the reference terminal, how to reduce the influence of Tx-Rx delay error on the positioning result without adding the reference terminal becomes a problem to be solved.

Disclosure of Invention

The embodiment of the invention aims to provide a positioning method, a positioning device, electronic equipment and a computer readable storage medium, so as to reduce the influence of Tx-Rx delay errors on a positioning result on the premise of not increasing a reference terminal and further improve the positioning precision. The specific technical scheme is as follows:

in a first aspect, an embodiment of the present invention provides a positioning method, including:

acquiring RSRP (Reference Signal Receiving Power ) and TOA (time of arrival) of equipment to be positioned when receiving signals sent by a plurality of base stations at the current moment, inputting the RSRP into a cyclic neural network obtained by pre-training to obtain a first predicted position of the equipment to be positioned at the current moment, wherein the cyclic neural network obtained by pre-training is obtained by training a plurality of training sample pairs, each training sample pair comprises at least three RSRP samples, and each RSRP sample carries a corresponding real position;

Determining the transmitting-receiving Tx-Rx delay error of each base station at the current moment according to the first predicted position of the equipment to be positioned at the current moment and the TOA;

performing error elimination processing on TOAs according to Tx-Rx delay errors of each base station at the current moment to obtain TOAs after error elimination of each base station at the current moment;

and determining the position coordinates of the equipment to be positioned at the current moment based on the TOA of each base station at the current moment after the errors are eliminated.

Optionally, after performing error cancellation processing on the TOA according to the Tx-Rx delay error of each base station at the current time to obtain the TOA after the cancellation error of each base station at the current time, the method further includes:

aiming at each base station, acquiring TOA of the base station at the moment which is the last moment of the current moment and is after error elimination;

judging whether the absolute value of the difference value between the TOA of the base station after the error elimination at the current moment and the TOA after the error elimination at the moment before the current moment is larger than a first preset threshold value or not;

if the absolute value of the difference between the TOA of the base station after the error elimination at the current moment and the TOA of the base station after the error elimination at the moment is smaller than or equal to a first preset threshold value, executing the step of determining the position coordinate of the equipment to be positioned at the current moment based on the TOA of each base station after the error elimination at the current moment;

If the absolute value of the difference between the TOA after the error elimination of the base station at the current moment and the TOA after the error elimination of the base station at the moment is greater than a first preset threshold value, carrying out smoothing treatment on the TOA after the error elimination of the base station at the current moment based on the first preset threshold value;

and determining the position coordinates of the equipment to be positioned at the current moment based on the TOA of each base station after the smoothing processing at the current moment.

Optionally, after determining the position coordinates of the device to be positioned at the current moment based on the TOA of each base station at the current moment after the error is eliminated, the method further includes:

acquiring a distance value between equipment to be positioned and each base station at the last moment of the current moment and position coordinates of each base station, wherein the distance value between the equipment to be positioned and each base station is obtained based on signal strength or TOA (time of arrival) between the equipment to be positioned and each base station;

obtaining a second predicted position of the equipment to be positioned at the current moment through a Kalman filtering algorithm based on the distance value between the equipment to be positioned and each base station at the last moment of the current moment, the position coordinates of each base station and the TOA of each base station after the error elimination at the current moment;

Based on the second predicted position of the equipment to be positioned at the current moment and the position coordinates of each base station, obtaining a third predicted position of the equipment to be positioned at the current moment through a Taylor iterative algorithm;

and determining the corrected position coordinate of the equipment to be positioned at the current moment based on the position coordinate of the equipment to be positioned at the current moment and the third predicted position of the equipment to be positioned at the current moment.

Optionally, determining the corrected position coordinate of the device to be located at the current time based on the position coordinate of the device to be located at the current time and the third predicted position of the device to be located at the current time includes:

judging whether the absolute value of the difference value between the position coordinate of the equipment to be positioned at the current moment and the third predicted position of the equipment to be positioned at the current moment is larger than a second preset threshold value or not;

if the position of the equipment to be positioned is larger than the current position coordinate, taking the third predicted position of the equipment to be positioned at the current time as the corrected position coordinate of the equipment to be positioned at the current time;

otherwise, the position coordinates of the equipment to be positioned at the current moment and the third predicted position of the equipment to be positioned at the current moment are weighted, so that the corrected position coordinates of the equipment to be positioned at the current moment are obtained.

In a second aspect, an embodiment of the present invention further provides a positioning device, including:

the input module is used for acquiring RSRP and TOA when the equipment to be positioned receives signals sent by a plurality of base stations at the current moment, inputting the RSRP into a circulating neural network obtained by pre-training to obtain a first predicted position of the equipment to be positioned at the current moment, wherein the circulating neural network obtained by pre-training is obtained by training a plurality of training sample pairs, each training sample pair comprises at least three RSRP samples, and each RSRP sample carries a corresponding real position;

the delay error determining module is used for determining the transmitting-receiving Tx-Rx delay error of each base station at the current moment according to the first predicted position of the equipment to be positioned at the current moment and the TOA;

the error elimination module is used for carrying out error elimination processing on the TOAs according to the Tx-Rx delay errors of the base stations at the current moment to obtain the TOAs after the errors of the base stations at the current moment are eliminated;

the first positioning module is used for determining the position coordinates of the equipment to be positioned at the current moment based on the TOA of each base station at the current moment after the errors are eliminated.

Optionally, the apparatus further comprises:

The first acquisition module is used for carrying out error elimination processing on the TOAs according to the Tx-Rx delay errors of each base station at the current moment to obtain the TOAs after the errors of each base station at the current moment are eliminated, and acquiring the TOAs after the errors of the base station at the moment which is the last moment of the current moment for each base station;

the judging module is used for judging whether the absolute value of the difference value between the TOA of the base station after the error elimination at the current moment and the TOA after the error elimination at the moment above the current moment is larger than a first preset threshold value; if yes, triggering a smoothing processing module, and if no, triggering a first positioning module;

the smoothing processing module is used for carrying out smoothing processing on the TOA of the base station after the error elimination at the current moment based on a first preset threshold value;

and the second positioning module is also used for determining the position coordinates of the equipment to be positioned at the current moment based on the TOA of each base station after the smoothing processing at the current moment.

Optionally, the apparatus further comprises:

the second acquisition module is used for acquiring a distance value between the equipment to be positioned and each base station at the last moment of the current moment and the position coordinates of each base station after determining the position coordinates of the equipment to be positioned at the current moment based on the TOA of each base station at the current moment after eliminating errors, wherein the distance value between the equipment to be positioned and each base station is obtained based on the signal strength or the TOA between the equipment to be positioned and each base station;

The first prediction module is used for obtaining a second predicted position of the equipment to be positioned at the current moment through a Kalman filtering algorithm based on the distance value between the equipment to be positioned at the moment and each base station at the moment, the position coordinates of each base station and TOA of each base station after error elimination at the moment;

the second prediction module is used for obtaining a third predicted position of the equipment to be positioned at the current moment through a Taylor iterative algorithm based on a second predicted position of the equipment to be positioned at the current moment and position coordinates of each base station;

and the correction module is used for determining corrected position coordinates of the equipment to be positioned at the current moment based on the position coordinates of the equipment to be positioned at the current moment and the third predicted position of the equipment to be positioned at the current moment.

Optionally, the correction module is specifically configured to:

judging whether the absolute value of the difference value between the position coordinate of the equipment to be positioned at the current moment and the third predicted position of the equipment to be positioned at the current moment is larger than a second preset threshold value or not; if the position of the equipment to be positioned is larger than the current position coordinate, taking the third predicted position of the equipment to be positioned at the current time as the corrected position coordinate of the equipment to be positioned at the current time; otherwise, the position coordinates of the equipment to be positioned at the current moment and the third predicted position of the equipment to be positioned at the current moment are weighted, so that the corrected position coordinates of the equipment to be positioned at the current moment are obtained.

In a third aspect, an embodiment of the present invention further provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, where the processor, the communication interface, and the memory complete communication with each other through the communication bus;

a memory for storing a computer program;

a processor for implementing the method steps of any of the above first aspects when executing a program stored on a memory.

In a fourth aspect, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored therein, which when executed by a processor implements the method steps of any of the first aspects described above.

Embodiments of the present invention also provide a computer program product comprising instructions which, when run on a computer, cause the computer to perform the method steps of any of the above-described first aspects.

The embodiment of the invention has the beneficial effects that:

the positioning method, the device, the electronic equipment and the computer readable storage medium provided by the embodiment of the invention can firstly acquire RSRP and TOA when the equipment to be positioned receives signals sent by a plurality of base stations at the current moment, then input the RSRP into a circulating neural network obtained by pre-training to obtain a first predicted position of the equipment to be positioned at the current moment, wherein the circulating neural network obtained by pre-training is obtained by training a plurality of training sample pairs, each training sample pair comprises at least three RSRP samples, and each RSRP sample carries a corresponding real position; determining Tx-Rx delay errors of all base stations at the current moment according to the first predicted position and TOA of the equipment to be positioned at the current moment; performing error elimination processing on the TOAs according to the Tx-Rx delay errors of each base station at the current moment to obtain TOAs after error elimination of each base station at the current moment; and finally, determining the position coordinates of the equipment to be positioned at the current moment based on the TOA of each base station at the current moment after the errors are eliminated. Therefore, the Tx-Rx delay error can be eliminated by training to obtain the circulating neural network, a reference terminal is not required to be specially arranged, and the Tx-Rx delay error is not required to be eliminated through the reference terminal in the positioning process, so that the influence of the Tx-Rx delay error on a positioning result can be reduced on the premise of not increasing the reference terminal, the positioning precision is further improved, and the cost for arranging the reference terminal can be saved. Of course, not all of the above advantages need be achieved simultaneously in the practice of any one product or method of the present invention.

Drawings

In order to more clearly illustrate the embodiments of the application or the technical solutions in the prior art, the drawings used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the application, and other embodiments may be obtained according to these drawings to those skilled in the art.

FIG. 1 is a flowchart of a first implementation of a positioning method according to an embodiment of the present application;

FIG. 2 is a flow chart of a second implementation of a positioning method according to an embodiment of the present application;

FIG. 3 is a flow chart of a third implementation of a positioning method according to an embodiment of the present application;

FIG. 4 is a flowchart of a fourth implementation of a positioning method according to an embodiment of the present application;

FIG. 5 is a schematic diagram of a positioning device according to an embodiment of the present application;

fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. Based on the embodiments of the present application, all other embodiments obtained by the person skilled in the art based on the present application are included in the scope of protection of the present application.

In order to solve the problems in the prior art, the embodiment of the invention provides a positioning method, a positioning device, an electronic device and a computer readable storage medium, so as to reduce the influence of Tx-Rx delay errors on a positioning result on the premise of not increasing a reference terminal and further improve the positioning precision.

Next, a method for positioning according to an embodiment of the present invention will be described, as shown in fig. 1, which is a flowchart of a first implementation of a positioning method according to an embodiment of the present invention, where the method may include:

s110, acquiring RSRP and TOA when equipment to be positioned receives signals sent by a plurality of base stations at the current moment, inputting the RSRP into a cyclic neural network obtained by pre-training to obtain a first predicted position of the equipment to be positioned at the current moment, wherein the cyclic neural network obtained by pre-training is obtained by training a plurality of training sample pairs, each training sample pair comprises at least three RSRP samples, and each RSRP sample carries a corresponding real position;

s120, determining Tx-Rx delay errors of all base stations at the current moment according to the first predicted position and TOA of the equipment to be positioned at the current moment;

s130, performing error elimination processing on TOAs according to Tx-Rx delay errors of all base stations at the current moment to obtain TOAs after error elimination of all base stations at the current moment;

And S140, determining the position coordinates of the equipment to be positioned at the current moment based on the TOA of each base station at the current moment after the errors are eliminated.

In some examples, the device to be located may typically communicate with a base station surrounding the environment when locating, and during the communication, the device to be located may send a signal to the base station, and the base station may also send a signal to the device to be located. When the equipment to be positioned receives signals sent by the base stations, RSRP of the signals sent by the base stations can be obtained. For example, assuming that the device to be located receives signals sent by three base stations at the current time, three RSRP may be obtained when the device to be located receives signals sent by three base stations at the current time;

in still other examples, when the base station sends a signal to the to-be-located device, the signal may carry a sending time, and when the to-be-located device receives the signal sent by each base station, the to-be-located device may obtain the sending time of each base station, and may also obtain the receiving time of the to-be-located device that receives the signal sent by each base station, so that the TOA that the signal sent by each base station reaches the to-be-located device may be obtained. For example, three TOAs of the device to be located, where three signals sent by the three base stations reach the device to be located, may be acquired.

After the RSRP of the signals sent by each base station and the TOA of the to-be-positioned device are obtained, the RSRP may be input into a pre-trained cyclic neural network, and the pre-trained cyclic neural network predicts the position of the to-be-positioned device at the current time based on the RSRP as the first predicted position.

The cyclic neural network obtained through pre-training is obtained through training of a plurality of training sample pairs, each training sample pair comprises at least three RSRP samples, and each RSRP sample carries a corresponding real position. In the training process, firstly, an RSRP sample is input into a preset circulating neural network, the circulating neural network outputs a predicted position based on the RSRP sample, then the predicted position is corresponding to the real position of the RSRP sample, errors of the RSRP sample and the real position are determined, then parameters of the preset circulating neural network are adjusted based on the errors, then the RSRP sample is input into the circulating neural network after the parameters are adjusted, the circulating neural network after the parameters are adjusted outputs a predicted position, the predicted position is calculated to be compared with the real position corresponding to the RSRP sample, the errors of the predicted position and the real position corresponding to the RSRP sample are determined, and then the steps of adjusting parameters of the preset circulating neural network based on the errors are repeated, so that the parameters of the circulating neural network are adjusted in multiple iterations, and finally training is finished when the predicted positions output by the circulating neural network after the parameters are adjusted multiple times reach end conditions, and the training completed circulating neural network can be obtained. Wherein, the RSRP sample and the carried corresponding real position are acquired in advance.

After the first predicted position of the equipment to be positioned, which is output by the pre-trained and obtained cyclic neural network, at the current moment is obtained, in order to more accurately position the equipment to be positioned, at this moment, the Tx-Rx delay error of each base station at the current moment can be determined according to the first predicted position of the equipment to be positioned at the current moment and the TOA.

In some examples, each base station has a corresponding Tx-Rx delay error because the Tx-Rx delay error is generated during communication between the base station and the device to be located.

For each base station, its corresponding Tx-Rx delay errorThe method can be calculated by the following formula:

wherein, TOA _i TOA, x for the ith base station and device to be located _i And y _i For the position coordinates of the i-th base station,and->And E is a preset positioning error expectation, and c is a signal transmission rate for the first position of the equipment to be positioned at the current moment.

In some examples, for each base station, multiple RSRP and TOA may be collected at the current time, and thus multiple Tx-Rx delay errors for that base station at the current time may be obtained, then by the following formula:

calculating an average value of the plurality of Tx-Rx delay errors Further, the average value of the multiple Tx-Rx delay errors +.>As the Tx-Rx delay error of the base station at the current time. Where m is the number of samples at the current time.

After the Tx-Rx delay errors of all the base stations at the current moment are obtained, in order to realize more accurate positioning of the equipment to be positioned, the TOA of each base station can be subjected to error elimination processing according to the Tx-Rx delay errors of all the base stations at the current moment, so that the TOA of each base station after the errors are eliminated at the current moment is obtained;

in some examples, when the TOA of each base station is subjected to error cancellation, the Tx-Rx delay error of the base station at the current time may be subtracted from the TOA of the base station, so that the TOA of the base station after the error cancellation at the current time may be obtained.

After the TOA of each base station after the error elimination at the current moment is obtained through the steps, the position coordinate of the equipment to be positioned at the current moment can be determined based on the TOA of each base station after the error elimination at the current moment.

Specifically, the TOA of each base station after the error elimination at the current moment can be multiplied by the propagation speed of the signal to obtain the distance between the equipment to be positioned and each base station, then a circle can be drawn by taking the distance between each base station and the equipment to be positioned as a radius and the position of each base station as the center of a circle, and a plurality of circles can be obtained, wherein the intersection point of the circles is the position coordinate of the equipment to be positioned at the current moment.

According to the positioning method, RSRP and TOA of equipment to be positioned when receiving signals sent by a plurality of base stations at the current moment are firstly obtained, then RSRP is input into a cyclic neural network obtained through pre-training, and a first predicted position of the equipment to be positioned at the current moment is obtained, wherein the cyclic neural network obtained through pre-training is obtained through training of a plurality of training sample pairs, each training sample pair comprises at least three RSRP samples, and each RSRP sample carries a corresponding real position; determining Tx-Rx delay errors of all base stations at the current moment according to the first predicted position and TOA of the equipment to be positioned at the current moment; performing error elimination processing on the TOAs according to the Tx-Rx delay errors of each base station at the current moment to obtain TOAs after error elimination of each base station at the current moment; and finally, determining the position coordinates of the equipment to be positioned at the current moment based on the TOA of each base station at the current moment after the errors are eliminated. Therefore, the Tx-Rx delay error can be eliminated by training to obtain the circulating neural network, a reference terminal is not required to be specially arranged, and the Tx-Rx delay error is not required to be eliminated through the reference terminal in the positioning process, so that the influence of the Tx-Rx delay error on a positioning result can be reduced on the premise of not increasing the reference terminal, the positioning precision is further improved, and the cost for arranging the reference terminal can be saved.

On the basis of a positioning method shown in fig. 1, the embodiment of the present invention further provides a possible implementation manner, as shown in fig. 2, which is a flowchart of a second implementation manner of a positioning method of the embodiment of the present invention, where the method may include:

s210, acquiring RSRP and TOA when equipment to be positioned receives signals sent by a plurality of base stations at the current moment, inputting the RSRP into a cyclic neural network obtained by pre-training to obtain a first predicted position of the equipment to be positioned at the current moment, wherein the cyclic neural network obtained by pre-training is obtained by training a plurality of training sample pairs, each training sample pair comprises at least three RSRP samples, and each RSRP sample carries a corresponding real position;

s220, determining Tx-Rx delay errors of all base stations at the current moment according to the first predicted position and TOA of the equipment to be positioned at the current moment;

s230, performing error elimination processing on TOAs according to Tx-Rx delay errors of all base stations at the current moment to obtain TOAs after error elimination of all base stations at the current moment;

s240, for each base station, acquiring TOA of the base station after error elimination at the moment previous to the current moment;

S250, judging whether the absolute value of the difference value between the TOA of the base station after the error elimination at the current moment and the TOA after the error elimination at the moment above the current moment is larger than a first preset threshold value; if yes, go to step S270, if no, go to step S260;

and S260, determining the position coordinates of the equipment to be positioned at the current moment based on the TOA of each base station at the current moment after the errors are eliminated.

S270, based on the first preset threshold, the TOA of the base station after the error elimination at the current moment is subjected to smoothing processing.

S280, determining the position coordinates of the equipment to be positioned at the current moment based on the TOA of each base station after the smoothing processing at the current moment.

In some examples, before obtaining the TOA of each base station after the error cancellation at the current time through steps S210 to S230, the TOA of each base station after the error cancellation at a plurality of times at the current time may also be obtained through steps S210 to S230.

In still other examples, after obtaining the position coordinates of the to-be-positioned device at the current moment, the observed quantity of the base station for the same position point fluctuates up and down due to clock jitter, and before the current moment, the position coordinates of the to-be-positioned device at the last moment before the current moment can also be obtained by using the positioning method of the embodiment of the invention, in order to further reduce positioning errors and improve positioning accuracy, the TOA can be subjected to error elimination processing according to the Tx-Rx delay errors of each base station at the current moment, and after obtaining the TOA of each base station after the elimination errors of the current moment, the TOA of each base station after the elimination errors of the last moment of the current moment can be obtained.

Then, for each base station, judging whether the absolute value of the difference between the TOA of the base station after the error elimination at the current moment and the TOA after the error elimination at the moment above the current moment is larger than a first preset threshold value

For example, assume that the TOA of the ith base station after the error cancellation at the current time isThe TOA of the ith base station after error elimination at the time immediately preceding the current time is +.>

It can be judged thatMinus->Whether the absolute value of the difference of (c) is greater than a first preset threshold. If the time difference is smaller than or equal to the time difference, the TOA of the base station after the error elimination is not needed to be subjected to smoothing processing, and the position coordinate of the equipment to be positioned at the current time can be determined based on the TOA of each base station after the error elimination at the current time.

If the difference is larger than the first preset threshold, the TOA after the error elimination of the base station at the current moment can be subjected to smoothing processing;

specifically, it can be judged thatAnd->The magnitude relation of (1) when->Then by the following formula

For a pair ofPerforming smoothing to obtain smoothed TOA of the base station at the current time, namely replacing the TOA by using the value obtained by adding the first preset threshold to the error-removed TOA of the base station at the time immediately before the current time >Thereby realizing the smoothing of the TOA of the base station after the error elimination at the current moment, wherein +.>The method comprises the steps that the TOA after smoothing processing at the current moment of an ith base station is used, and a factor is a first preset threshold;

when (when)Then by the following formula

For a pair ofPerforming smoothing to obtain smoothed TOA of the base station at the current moment, namely replacing the TOA by subtracting a value obtained by subtracting a first preset threshold from the error-removed TOA of the previous moment>Thereby realizing the smoothing processing of the TOA of the base station after the error elimination at the current moment.

According to the embodiment of the invention, the position coordinates of the equipment to be positioned at the current moment can be positioned based on the TOA values after error elimination at a plurality of moments, and the positioning precision of the equipment to be positioned can be further improved.

It is understood that steps S210 to S230 and S260 in the embodiment of the present invention are the same as or similar to steps S110 to S140 in the first embodiment, and will not be described herein.

On the basis of a positioning method shown in fig. 1, the embodiment of the present invention further provides a possible implementation manner, as shown in fig. 3, which is a flowchart of a third implementation manner of a positioning method of the embodiment of the present invention, where the method may include:

S310, acquiring RSRP and TOA when equipment to be positioned receives signals sent by a plurality of base stations at the current moment, inputting the RSRP into a cyclic neural network obtained by pre-training to obtain a first predicted position of the equipment to be positioned at the current moment, wherein the cyclic neural network obtained by pre-training is obtained by training a plurality of training sample pairs, each training sample pair comprises at least three RSRP samples, and each RSRP sample carries a corresponding real position;

s320, determining the transmitting-receiving Tx-Rx delay error of each base station at the current moment according to the first predicted position of the equipment to be positioned at the current moment and the TOA;

s330, performing error elimination processing on the TOAs according to the Tx-Rx delay errors of each base station at the current moment to obtain TOAs after error elimination of each base station at the current moment;

s340, determining the position coordinates of the equipment to be positioned at the current moment based on the TOA of each base station at the current moment after the errors are eliminated.

S350, obtaining a distance value between equipment to be positioned and each base station at the last moment of the current moment and position coordinates of each base station, wherein the distance value between the equipment to be positioned and each base station is obtained based on signal strength or TOA between the equipment to be positioned and each base station;

S360, obtaining a second predicted position of the equipment to be positioned at the current moment through a Kalman filtering algorithm based on the distance value between the equipment to be positioned and each base station at the moment, the position coordinates of each base station and the TOA of each base station after the error elimination at the moment;

s370, obtaining a third predicted position of the equipment to be positioned at the current moment through a Taylor iterative algorithm based on the second predicted position of the equipment to be positioned at the current moment and the position coordinates of each base station;

s380, determining the corrected position coordinate of the equipment to be positioned at the current moment based on the position coordinate of the equipment to be positioned at the current moment and the third predicted position of the equipment to be positioned at the current moment.

In some examples, after determining the position coordinate of the to-be-positioned device at the current time based on the TOA of each base station after the error is eliminated at the current time, in order to further improve the positioning accuracy of the to-be-positioned device, the position coordinate of the to-be-positioned device at the current time may be corrected by combining the TOA of each base station after the error is eliminated at the current time and the distance value between the to-be-positioned device and each base station at the last time of the current time.

Specifically, the distance value between the device to be positioned and each base station at the previous moment of the current moment and the position coordinates of each base station can be obtained first. The distance value between the equipment to be positioned and each base station is obtained based on the signal strength or TOA between the equipment to be positioned and each base station;

for example, assuming that there are N base stations, the distance value between the N base stations and the device to be located is r ₁ ,r ₂ ,…,r _i ,…,r _N 。

And obtaining a second predicted position of the equipment to be positioned at the current moment through a Kalman filtering algorithm based on the distance value between the equipment to be positioned and each base station at the last moment of the current moment, the position coordinates of each base station and the TOA of each base station after the error elimination at the current moment.

Specifically, the following matrix may be established based on the distance value between the device to be located and each base station at the last time of the current time and the position coordinates of each base station:

wherein, (x) _i ,y _i ) Is the coordinates of the i-th base station;

then through the following matrix formula:

obtaining the position information of the equipment to be positioned at the moment which is the last time of the current moment

Obtaining the position information of the equipment to be positioned at the moment which is the last time of the current momentAfter that, the position information of the device to be positioned at the moment immediately preceding the current moment can be +. >As the control variable of the Kalman filtering algorithm, the Kalman filtering algorithm is adopted to calculate the second predicted position of the equipment to be positioned at the current moment

It is understood that the kalman filtering algorithm is an algorithm in the prior art, and will not be described herein.

After the second predicted position of the equipment to be positioned at the current moment is obtained through calculation, the second predicted position of the equipment to be positioned at the current moment can be used as an initial value of a Taylor iterative algorithm, and then iterative calculation is carried out by using the Taylor iterative algorithm to obtain a third predicted position of the equipment to be positioned at the current moment.

In the iterative calculation process, once per iterative calculation, the positioning error is adjusted once by multipleAdjusting the positioning error for the second time, and when the sum of the absolute values of the positioning error of the abscissa and the positioning error of the ordinate obtained by calculation after one-time adjustment is smaller than a preset positioning error threshold value, calculating the position coordinate of the positioning error as a third predicted position of the equipment to be positioned at the current moment; for example, the positioning error delta of the abscissa is calculated in the jth iteration _x And positioning error delta of ordinate _y If the sum of the absolute values of the (b) is smaller than the preset positioning error threshold, the positioning error delta of the abscissa at the jth iteration can be calculated _x And positioning error delta of ordinate _y And the position coordinate of the j-1 th iteration is used as a third predicted position of the equipment to be positioned at the current moment. The preset positioning error threshold is empirically preset.

After the third predicted position of the device to be positioned at the current moment is obtained, the position coordinate of the device to be positioned at the current moment can be corrected by using the third predicted position, so that the corrected position coordinate of the device to be positioned at the current moment can be obtained.

It will be appreciated that the taylor iterative algorithm is also an algorithm in the prior art, and will not be described in detail here.

By the embodiment of the invention, the position of the equipment to be positioned at the moment which is the last moment of the current moment and the position of the equipment to be positioned at the current moment which is predicted by the cyclic neural network can be integrated together to determine the position coordinate of the equipment to be positioned, so that the position coordinate of the equipment to be positioned can be more accurately determined, and the accuracy of positioning the equipment to be positioned can be further improved.

It is to be understood that steps S310 to S340 in the embodiment of the present invention are the same as or similar to steps S110 to S140 in the first embodiment, and are not described herein.

On the basis of a positioning method shown in fig. 3, the embodiment of the present invention further provides a possible implementation manner, as shown in fig. 4, which is a flowchart of a fourth implementation manner of a positioning method of the embodiment of the present invention, where the method may include:

s3801, judging whether the absolute value of the difference value between the position coordinate of the equipment to be positioned at the current moment and the third predicted position of the equipment to be positioned at the current moment is larger than a second preset threshold value; if yes, execution proceeds to step S3802, otherwise execution proceeds to step S3803;

s3802, taking the third predicted position of the equipment to be positioned at the current moment as the corrected position coordinate of the equipment to be positioned at the current moment;

S3803, carrying out weighting processing on the position coordinate of the equipment to be positioned at the current moment and the third predicted position of the equipment to be positioned at the current moment to obtain the corrected position coordinate of the equipment to be positioned at the current moment.

In some examples, the embodiment of the invention further provides a possible implementation manner when the corrected position coordinate of the device to be positioned at the current moment is determined based on the position coordinate of the device to be positioned at the current moment and the third predicted position of the device to be positioned at the current moment.

Specifically, after the third predicted position of the device to be positioned at the current time is obtained, whether the absolute value of the difference between the position coordinate of the device to be positioned at the current time and the third predicted position of the device to be positioned at the current time is larger than a second preset threshold value or not can be judged; if the absolute value of the difference between the position coordinate of the equipment to be positioned at the current moment and the third predicted position of the equipment to be positioned at the current moment is smaller than or equal to a second preset threshold value, weighting processing can be carried out on the position coordinate of the equipment to be positioned at the current moment and the third predicted position of the equipment to be positioned at the current moment, and the position coordinate obtained after the weighting processing is used as the corrected position coordinate of the equipment to be positioned at the current moment.

Specifically, the following formula can be used:

weighting to obtain the corrected position coordinates of the equipment to be positioned at the current momentWherein (1)>Is set for waiting to locatePreparing a position coordinate at the current moment; />And a third predicted position of the equipment to be positioned at the current moment. RMSE _A For the root mean square error of the cyclic neural network obtained by training in advance, the error is RMSE _B The root mean square error is the root mean square error when the Kalman filtering algorithm and the Taylor iterative algorithm are adopted. The root mean square error of the cyclic neural network obtained through pre-training and the root mean square error of the cyclic neural network obtained through pre-training when a Kalman filtering algorithm and a Taylor iterative algorithm are respectively counted after corresponding data are used for testing in advance.

If the absolute value of the difference between the position coordinate of the device to be positioned at the current time and the third predicted position of the device to be positioned at the current time is greater than the second preset threshold, it is indicated that the prediction accuracy of the cyclic neural network obtained by pre-training is poor over time, and the error of the position coordinate of the device to be positioned at the current time obtained by step S340 is also greater, and at this time, the position coordinate of the device to be positioned at the current time obtained by step S340 is not suitable to be used as the final position coordinate of the device to be positioned. At this time, the third predicted position of the device to be positioned at the current time can be used as the corrected position coordinate of the device to be positioned at the current time; thereby, the correction of the position coordinate of the equipment to be positioned at the current moment can be realized.

By the embodiment of the invention, the influence of time factors on the prediction result can be fully considered, so that the positioning accuracy of the equipment to be positioned is further improved.

Corresponding to the above method embodiment, the embodiment of the present invention further provides a positioning device, as shown in fig. 5, which is a schematic structural diagram of a positioning device according to the embodiment of the present invention, where the device may include:

the input module 510 is configured to obtain RSRP and TOA when the device to be positioned receives signals sent by a plurality of base stations at a current time, input the RSRP into a cyclic neural network obtained by training in advance, and obtain a first predicted position of the device to be positioned at the current time, where the cyclic neural network obtained by training in advance is obtained by training a plurality of training sample pairs, each training sample pair includes at least three RSRP samples, and each RSRP sample carries a corresponding real position;

a delay error determining module 520, configured to determine a transmit-receive Tx-Rx delay error of each base station at the current time according to the first predicted position of the device to be located at the current time and the TOA;

an error elimination module 530, configured to perform error elimination processing on the TOA according to the Tx-Rx delay error of each base station at the current time, so as to obtain the TOA after the error elimination of each base station at the current time;

The first positioning module 540 is configured to determine, based on the TOA of each base station at the current time after the error is eliminated, a position coordinate of the device to be positioned at the current time.

According to the positioning device, RSRP and TOA of equipment to be positioned when receiving signals sent by a plurality of base stations at the current moment are firstly obtained, then RSRP is input into a cyclic neural network obtained through pre-training, and a first predicted position of the equipment to be positioned at the current moment is obtained, wherein the cyclic neural network obtained through pre-training is obtained through training of a plurality of training sample pairs, each training sample pair comprises at least three RSRP samples, and each RSRP sample carries a corresponding real position; determining Tx-Rx delay errors of all base stations at the current moment according to the first predicted position and TOA of the equipment to be positioned at the current moment; performing error elimination processing on the TOAs according to the Tx-Rx delay errors of each base station at the current moment to obtain TOAs after error elimination of each base station at the current moment; and finally, determining the position coordinates of the equipment to be positioned at the current moment based on the TOA of each base station at the current moment after the errors are eliminated. Therefore, the Tx-Rx delay error can be eliminated by training to obtain the circulating neural network, a reference terminal is not required to be specially arranged, and the Tx-Rx delay error is not required to be eliminated through the reference terminal in the positioning process, so that the influence of the Tx-Rx delay error on a positioning result can be reduced on the premise of not increasing the reference terminal, the positioning precision is further improved, and the cost for arranging the reference terminal can be saved.

In some examples, the apparatus further comprises:

the judging module is used for judging whether the absolute value of the difference value between the TOA of the base station after the error elimination at the current moment and the TOA after the error elimination at the moment above the current moment is larger than a first preset threshold value; if yes, triggering a smoothing module, if no, triggering a first positioning module 540;

In some examples, the apparatus further comprises:

In some examples, the correction module is specifically configured to:

The embodiment of the invention also provides an electronic device, as shown in fig. 6, which comprises a processor 601, a communication interface 602, a memory 603 and a communication bus 604, wherein the processor 601, the communication interface 602 and the memory 603 complete communication with each other through the communication bus 604,

a memory 603 for storing a computer program;

the processor 601 is configured to implement the positioning method according to any of the above embodiments when executing the program stored in the memory 603, for example, the following steps may be implemented:

acquiring RSRP and TOA when equipment to be positioned receives signals sent by a plurality of base stations at the current moment, inputting the RSRP into a cyclic neural network obtained by pre-training to obtain a first predicted position of the equipment to be positioned at the current moment, wherein the cyclic neural network obtained by pre-training is obtained by training a plurality of training sample pairs, each training sample pair comprises at least three RSRP samples, and each RSRP sample carries a corresponding real position;

The electronic equipment provided by the embodiment of the invention can firstly acquire the RSRP and TOA when the equipment to be positioned receives signals sent by a plurality of base stations at the current moment, and then input the RSRP into a cyclic neural network obtained by training in advance to obtain a first predicted position of the equipment to be positioned at the current moment, wherein the cyclic neural network obtained by training in advance is obtained by training a plurality of training sample pairs, each training sample pair comprises at least three RSRP samples, and each RSRP sample carries a corresponding real position; determining Tx-Rx delay errors of all base stations at the current moment according to the first predicted position and TOA of the equipment to be positioned at the current moment; performing error elimination processing on the TOAs according to the Tx-Rx delay errors of each base station at the current moment to obtain TOAs after error elimination of each base station at the current moment; and finally, determining the position coordinates of the equipment to be positioned at the current moment based on the TOA of each base station at the current moment after the errors are eliminated. Therefore, the Tx-Rx delay error can be eliminated by training to obtain the circulating neural network, a reference terminal is not required to be specially arranged, and the Tx-Rx delay error is not required to be eliminated through the reference terminal in the positioning process, so that the influence of the Tx-Rx delay error on a positioning result can be reduced on the premise of not increasing the reference terminal, the positioning precision is further improved, and the cost for arranging the reference terminal can be saved.

The communication bus mentioned above for the electronic devices may be a peripheral component interconnect standard (Peripheral Component Interconnect, PCI) bus or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, etc. The communication bus may be classified as an address bus, a data bus, a control bus, or the like. For ease of illustration, the figures are shown with only one bold line, but not with only one bus or one type of bus.

The communication interface is used for communication between the electronic device and other devices.

The Memory may include random access Memory (Random Access Memory, RAM) or may include Non-Volatile Memory (NVM), such as at least one disk Memory. Optionally, the memory may also be at least one memory device located remotely from the aforementioned processor.

The processor may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; but also digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

In yet another embodiment of the present invention, there is further provided a computer readable storage medium having a computer program stored therein, which when executed by a processor, implements the positioning method shown in any of the above embodiments, for example, the following steps may be implemented:

The computer readable storage medium provided by the embodiment of the invention can firstly acquire RSRP and TOA when equipment to be positioned receives signals sent by a plurality of base stations at the current moment, then input the RSRP into a cyclic neural network obtained by pre-training to obtain a first predicted position of the equipment to be positioned at the current moment, wherein the cyclic neural network obtained by pre-training is obtained by training a plurality of training sample pairs, each training sample pair comprises at least three RSRP samples, and each RSRP sample carries a corresponding real position; determining Tx-Rx delay errors of all base stations at the current moment according to the first predicted position and TOA of the equipment to be positioned at the current moment; performing error elimination processing on the TOAs according to the Tx-Rx delay errors of each base station at the current moment to obtain TOAs after error elimination of each base station at the current moment; and finally, determining the position coordinates of the equipment to be positioned at the current moment based on the TOA of each base station at the current moment after the errors are eliminated. Therefore, the Tx-Rx delay error can be eliminated by training to obtain the circulating neural network, a reference terminal is not required to be specially arranged, and the Tx-Rx delay error is not required to be eliminated through the reference terminal in the positioning process, so that the influence of the Tx-Rx delay error on a positioning result can be reduced on the premise of not increasing the reference terminal, the positioning precision is further improved, and the cost for arranging the reference terminal can be saved.

In a further embodiment of the present invention, there is also provided a computer program product containing instructions which, when run on a computer, cause the computer to perform the positioning method of any of the embodiments described above, for example, the steps of:

The computer program product comprising the instruction provided by the embodiment of the invention can firstly acquire RSRP and TOA when the equipment to be positioned receives signals sent by a plurality of base stations at the current moment, then input the RSRP into a cyclic neural network obtained by pre-training to obtain a first predicted position of the equipment to be positioned at the current moment, wherein the cyclic neural network obtained by pre-training is obtained by training a plurality of training sample pairs, each training sample pair comprises at least three RSRP samples, and each RSRP sample carries a corresponding real position; determining Tx-Rx delay errors of all base stations at the current moment according to the first predicted position and TOA of the equipment to be positioned at the current moment; performing error elimination processing on the TOAs according to the Tx-Rx delay errors of each base station at the current moment to obtain TOAs after error elimination of each base station at the current moment; and finally, determining the position coordinates of the equipment to be positioned at the current moment based on the TOA of each base station at the current moment after the errors are eliminated. Therefore, the Tx-Rx delay error can be eliminated by training to obtain the circulating neural network, a reference terminal is not required to be specially arranged, and the Tx-Rx delay error is not required to be eliminated through the reference terminal in the positioning process, so that the influence of the Tx-Rx delay error on a positioning result can be reduced on the premise of not increasing the reference terminal, the positioning precision is further improved, and the cost for arranging the reference terminal can be saved.

In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, produces a flow or function in accordance with embodiments of the present invention, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another, for example, by wired (e.g., coaxial cable, optical fiber, digital Subscriber Line (DSL)), or wireless (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains an integration of one or more available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid State Disk (SSD)), etc.

It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

In this specification, each embodiment is described in a related manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for apparatus, electronic devices, computer-readable storage media, and computer program product embodiments containing instructions, the description is relatively simple as it is substantially similar to method embodiments, as relevant points are found in the partial description of method embodiments.

The foregoing description is only of the preferred embodiments of the present invention and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.

Claims

1. A method of positioning, the method comprising:

acquiring Reference Signal Receiving Power (RSRP) and arrival Time (TOA) of equipment to be positioned when receiving signals sent by a plurality of base stations at the current moment, inputting the RSRP into a circulating neural network obtained by pre-training to obtain a first predicted position of the equipment to be positioned at the current moment, wherein the circulating neural network obtained by pre-training is obtained by training a plurality of training sample pairs, each training sample pair comprises at least three RSRP samples, and each RSRP sample carries a corresponding real position;

determining a transmitting-receiving Tx-Rx delay error of each base station at the current moment according to a first predicted position of the equipment to be positioned at the current moment and the TOA;

performing error elimination processing on the TOA according to the Tx-Rx delay error of each base station at the current moment to obtain TOA after error elimination of each base station at the current moment;

And determining the position coordinates of the equipment to be positioned at the current moment based on the TOA of each base station at the current moment after the error is eliminated.

2. The method of claim 1, wherein after said error cancellation processing is performed on said TOAs based on Tx-Rx delay errors of each base station at said current time, to obtain said TOAs of each base station after said cancellation errors at said current time, said method further comprises:

for each base station, acquiring the TOA of the base station after error elimination at the moment previous to the current moment;

if the absolute value of the difference between the TOA of the base station after the error elimination at the current moment and the TOA after the error elimination at the moment which is the last to the current moment is smaller than or equal to a first preset threshold value, executing the step of determining the position coordinate of the equipment to be positioned at the current moment based on the TOA of each base station after the error elimination at the current moment;

if the absolute value of the difference between the TOA of the base station after the error elimination at the current moment and the TOA of the base station after the error elimination at the moment above the current moment is larger than a first preset threshold value, carrying out smoothing processing on the TOA of the base station after the error elimination at the current moment based on the first preset threshold value;

3. The method of claim 1, wherein after the determining the position coordinates of the device to be located at the current time based on the error-free TOAs of the respective base stations at the current time, the method further comprises:

acquiring a distance value between the equipment to be positioned and each base station and a position coordinate of each base station at the moment previous to the current moment, wherein the distance value between the equipment to be positioned and each base station is obtained based on signal strength or TOA between the equipment to be positioned and each base station;

obtaining a second predicted position of the equipment to be positioned at the current moment through a Kalman filtering algorithm based on a distance value between the equipment to be positioned and each base station at the moment, a position coordinate of each base station and TOA of each base station after error elimination at the moment;

4. A method according to claim 3, wherein said determining corrected position coordinates of said device to be located at said current time based on position coordinates of said device to be located at said current time and a third predicted position of said device to be located at said current time comprises:

if the position of the equipment to be positioned is larger than the current time, taking the third predicted position of the equipment to be positioned at the current time as the corrected position coordinate of the equipment to be positioned at the current time;

otherwise, carrying out weighting processing on the position coordinate of the equipment to be positioned at the current moment and the third predicted position of the equipment to be positioned at the current moment to obtain the corrected position coordinate of the equipment to be positioned at the current moment.

5. A positioning device, the device comprising:

the input module is used for acquiring RSRP and TOA when the equipment to be positioned receives signals sent by a plurality of base stations at the current moment, inputting the RSRP into a circulating neural network obtained by training in advance to obtain a first predicted position of the equipment to be positioned at the current moment, wherein the circulating neural network obtained by training in advance is obtained by training a plurality of training sample pairs, each training sample pair comprises at least three RSRP samples, and each RSRP sample carries a corresponding real position;

a delay error determining module, configured to determine a transmit-receive Tx-Rx delay error of each base station at the current time according to the first predicted position of the device to be located at the current time and the TOA;

the error elimination module is used for carrying out error elimination processing on the TOA according to the Tx-Rx delay error of each base station at the current moment to obtain TOA after error elimination of each base station at the current moment;

and the first positioning module is used for determining the position coordinates of the equipment to be positioned at the current moment based on the TOA of each base station after the error elimination at the current moment.

6. The apparatus of claim 5, wherein the apparatus further comprises:

a first obtaining module, configured to, after performing error cancellation processing on the TOA according to the Tx-Rx delay error of each base station at the current time, obtain, for each base station, a TOA after cancellation error of each base station at the current time, where the TOA after cancellation error of the base station at a time previous to the current time is obtained;

the judging module is used for judging whether the absolute value of the difference value between the TOA of the base station after the error elimination at the current moment and the TOA after the error elimination at the moment above the current moment is larger than a first preset threshold value; if yes, triggering a smoothing processing module, and if no, triggering the first positioning module;

the smoothing module is configured to smooth the TOA of the base station after the error is eliminated at the current moment based on the first preset threshold;

7. The apparatus of claim 5, wherein the apparatus further comprises:

The second obtaining module is configured to obtain, after determining, based on the TOA of each base station after the error cancellation at the current time, a distance value between the to-be-positioned device and each base station at a time immediately before the current time and a position coordinate of each base station, where the distance value between the to-be-positioned device and each base station is obtained based on signal strength or TOA between the to-be-positioned device and each base station;

the first prediction module is used for obtaining a second predicted position of the equipment to be positioned at the current moment through a Kalman filtering algorithm based on a distance value between the equipment to be positioned and each base station at the moment, a position coordinate of each base station and TOA of each base station after error elimination at the moment;

the second prediction module is used for obtaining a third predicted position of the equipment to be positioned at the current moment through a Taylor iterative algorithm based on a second predicted position of the equipment to be positioned at the current moment and the position coordinates of each base station;

8. The apparatus of claim 7, wherein the correction module is specifically configured to:

judging whether the absolute value of the difference value between the position coordinate of the equipment to be positioned at the current moment and the third predicted position of the equipment to be positioned at the current moment is larger than a second preset threshold value or not; if the position of the equipment to be positioned is larger than the current time, taking the third predicted position of the equipment to be positioned at the current time as the corrected position coordinate of the equipment to be positioned at the current time; otherwise, carrying out weighting processing on the position coordinate of the equipment to be positioned at the current moment and the third predicted position of the equipment to be positioned at the current moment to obtain the corrected position coordinate of the equipment to be positioned at the current moment.

9. The electronic equipment is characterized by comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory are communicated with each other through the communication bus;

a memory for storing a computer program;

a processor for carrying out the method steps of any one of claims 1-4 when executing a program stored on a memory.

10. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored therein a computer program which, when executed by a processor, implements the method steps of any of claims 1-4.