CN113573236A

CN113573236A - Method and device for evaluating confidence of positioning result

Info

Publication number: CN113573236A
Application number: CN202010353544.1A
Authority: CN
Inventors: 欧阳晔; 鹿岩; 张光辉; 蒋炜
Original assignee: Asiainfo Technologies China Inc
Current assignee: Asiainfo Technologies China Inc
Priority date: 2020-04-29
Filing date: 2020-04-29
Publication date: 2021-10-29
Anticipated expiration: 2040-04-29
Also published as: CN113573236B

Abstract

According to the method and the device for evaluating the confidence of the positioning result, when a user positions by using the measurement report, the confidence coefficient of the grid geographic scene type, the confidence coefficient of the data type in the grid fingerprint database, the confidence coefficient of the cell number, the confidence coefficient of the sample point number, the confidence coefficient of the adjacent cell data, the confidence coefficient of the average level variance and the confidence coefficient of the mobile type of the user are respectively obtained, and then the confidence of the positioning result for positioning the user by using the measurement report is calculated by using a preset formula. Therefore, the problem that an effective confidence evaluation cannot be performed on the positioning result of the user when the wireless MR of the user is used for positioning at present is solved.

Description

Method and device for evaluating confidence of positioning result

Technical Field

The present application relates to the field of communications technologies, and in particular, to a method and an apparatus for evaluating confidence of a positioning result.

Background

In daily life, when a user needs to be positioned, the position of the user can be acquired by acquiring a wireless Measurement Report (MR) of the user. The measurement report is a signal strength measurement report acquired by the base station, the wireless access network sends measurement control information to the mobile terminal, and the mobile terminal is connected with the terminal to be measured by the control information and sends the measurement report to the wireless access network. The specific longitude and latitude in the user MR is obtained by arranging the user call measurement report and utilizing the longitude and latitude of the base station, the level results of the serving cell and the adjacent cells in the measurement report and other information.

However, when a user wireless MR is collected for positioning, there are many uncertainties in the changes of the level in the user terminal MR and the surrounding environment due to the diversity of the wireless environment, and the fluctuation of the level in the MR reported by the user at intervals may cause the deviation between the longitude and latitude and the actual position of the user positioning to be large. In addition, if the characteristic values of the grid fingerprint library are not perfect and sufficient enough, the MR positioning accuracy is also influenced to a certain extent, and the deviation of the user positioning is caused. In some cases, it may be desirable to obtain more accurate MR positioning results, such as more detailed positioning to determine whether the user enters a mall. However, currently, when the user performs positioning by using wireless MR, an effective confidence evaluation cannot be made on the positioning result of the user. Therefore, in some cases where the accuracy of the positioning result of the user needs to be considered, a confidence level cannot be provided for the positioning result, and it cannot be determined whether the positioning result is accurate.

Disclosure of Invention

In view of the above, the present application provides a method and an apparatus for evaluating a confidence of a positioning result, so as to solve the problem that an effective confidence evaluation cannot be performed on a positioning result of a user when the user performs positioning by using a wireless MR.

In order to achieve the above purpose, the present application provides the following technical solutions:

the first aspect of the present application discloses a measurement report positioning confidence evaluation method, which includes:

aiming at the grids matched during user positioning, identifying the geographic scene type of the area where the grids are located, and obtaining the confidence coefficient of the geographic scene type of the area where the grids are located; the user is positioned by using the measurement report;

analyzing the grid fingerprint database corresponding to the grid to respectively obtain a confidence coefficient of the data type of the grid fingerprint database in the grid fingerprint database, a confidence coefficient of the cell number of the grid fingerprint database, a confidence coefficient of the sample point of the grid fingerprint database, a confidence coefficient of the neighbor cell data of the grid fingerprint database and a confidence coefficient of the average level variance of the grid fingerprint database;

identifying the movement type of the user and obtaining a confidence coefficient of the movement type of the user;

and calculating the confidence coefficient of the grid geographic scene type, the confidence coefficient of the data type, the confidence coefficient of the cell number, the confidence coefficient of the sample point number, the confidence coefficient of the neighboring cell data, the confidence coefficient of the average level variance and the confidence coefficient of the movement type of the user by using a preset formula to obtain the confidence coefficient of the positioning result for positioning the user by using the measurement report.

Optionally, in the method, the identifying the geographic scene type of the area where the grid is located and obtaining the confidence coefficient of the geographic scene type of the area where the grid is located includes:

if more than seventy percent of the pixels of the grid are displayed outdoors, the geographic scene type of the area where the grid is located is outdoors, and the confidence coefficient of the grid geographic scene is the first grid scene confidence coefficient;

if more than seventy percent of pixels of the grid are displayed indoors, the geographic scene type of the area where the grid is located is a building, and the confidence coefficient of the grid geographic scene is a second grid scene confidence coefficient;

if more than fifty percent of the pixels of the grid are displayed in the water area scene, the geographic scene type of the area where the grid is located is the water area, and the confidence coefficient of the grid geographic scene is the confidence coefficient of a third grid scene; wherein the first grid scene confidence coefficient is greater than the second grid scene confidence coefficient, which is greater than the third grid scene confidence coefficient.

Optionally, in the method, the analyzing the grid fingerprint database corresponding to the grid to obtain a confidence coefficient of a data type of the grid fingerprint database in the grid fingerprint database, a confidence coefficient of a number of cells in the grid fingerprint database, a confidence coefficient of a sample point in the grid fingerprint database, a confidence coefficient of neighboring cell data in the grid fingerprint database, and a confidence coefficient of an average level variance in the grid fingerprint database respectively includes:

if the data type of the grid fingerprint database is sweep frequency data or road test data, the confidence coefficient of the data type is a first data type confidence coefficient; if the data type of the grid fingerprint database is not sweep frequency data or road test data, the confidence coefficient of the data type is a second data type confidence coefficient; wherein the first data type confidence coefficient is greater than the second data type confidence coefficient;

if any two serving cells in the grid fingerprint library can occupy more than ninety percent of the sample points, the cell number confidence coefficient is a first cell number confidence coefficient; if no two serving cells in the grid fingerprint library can occupy more than ninety percent of the sample points, the grid fingerprint library cell number confidence coefficient is a second cell number confidence coefficient; wherein the first cell number confidence coefficient is greater than the second cell number confidence coefficient;

if the number of the sample points in the grid fingerprint database is more than 20, the confidence coefficient of the sample point is a first sample point confidence coefficient; if the number of the sample points in the grid fingerprint database is not more than 20, the confidence coefficient of the sample point is a second sample point confidence coefficient; wherein the first sample point confidence coefficient is greater than the second sample point confidence coefficient;

if the grid fingerprint database has data of adjacent cells, the confidence coefficient of the adjacent cell data is a first adjacent cell confidence coefficient, and if the grid fingerprint database does not have the data of the adjacent cells, the confidence coefficient of the adjacent cell data is a second adjacent cell confidence coefficient; the neighbor cell is another serving cell measured by the user in the measurement report except for the serving cell occupied by the user; the confidence coefficient of the first neighboring cell is greater than the confidence coefficient of the second neighboring cell;

if the level characteristic value variance of all sample points in the grid fingerprint database is smaller than 1, the confidence coefficient of the average level variance is a first average level confidence coefficient; and if the level characteristic value variance of all sample points in the grid fingerprint database is not less than 1, the confidence coefficient of the average level variance is a second average level confidence coefficient, wherein the first average level confidence coefficient is greater than the second average level confidence coefficient.

Optionally, in the method, the identifying the movement type of the user and obtaining the confidence coefficient of the movement type of the user includes:

if the user calling service occupies more than 3 service cells at a time and the maximum cell distance is more than 1 kilometer, the mobile type of the user is a mobile user, and the confidence coefficient of the mobile type of the user is a first user mobile type confidence coefficient;

if the user only occupies 1 service cell for one call service or the time of occupying one cell occupies more than eighty percent of the time of the call service, the mobile type of the user is a stable user, and the confidence coefficient of the mobile type of the user is the confidence coefficient of the mobile type of a second user;

if the mobile type of the user is identified to be neither a mobile user nor a stable user, the mobile type of the user is other types of users, and the confidence coefficient of the mobile type of the user is the confidence coefficient of the mobile type of a third user; wherein the second user movement type confidence coefficient is greater than the first user movement type confidence coefficient, and the first user movement type confidence coefficient is greater than the third user movement type confidence coefficient.

Optionally, in the method, the calculating, by using a preset formula, the confidence coefficient of the grid geographic scene type, the confidence coefficient of the data type, the confidence coefficient of the number of the cells, the confidence coefficient of the number of sample points, the confidence coefficient of the data in the neighboring cells, the confidence coefficient of the average level variance, and the confidence coefficient of the movement type of the user to obtain the confidence of the positioning result of positioning the user by using the measurement report includes:

summing the confidence coefficient of the data type, the confidence coefficient of the cell number of the grid fingerprint database, the confidence coefficient of the sample point number, the confidence coefficient of the data of the adjacent cell and the confidence coefficient of the average level variance, and multiplying the sum by one fifth to obtain a first result value;

and multiplying the first result value by the confidence coefficient of the grid geographic scene type and the confidence coefficient of the movement type of the user to obtain the confidence coefficient of the positioning result for positioning the user by using the measurement report.

Optionally, in the method, after obtaining the confidence level of the positioning result obtained by using the measurement report to position the user, the method further includes:

if the confidence of the positioning result of the user positioning by using the measurement report is higher than a preset threshold, judging that the positioning result of the user is accurate;

and if the confidence coefficient of the positioning result of the user positioning by using the measurement report is not higher than a preset threshold value, judging that the positioning result of the user is inaccurate.

A second aspect of the present application discloses an apparatus for evaluating confidence of a positioning result, including:

the first identification unit is used for identifying the geographic scene type of the area where the grid is located aiming at the grid matched during user positioning, and obtaining the confidence coefficient of the geographic scene type of the area where the grid is located; the user is positioned by using the measurement report;

the analysis unit is used for analyzing the grid fingerprint database corresponding to the grid to respectively obtain a confidence coefficient of the data type of the grid fingerprint database in the grid fingerprint database, a confidence coefficient of the cell number of the grid fingerprint database, a confidence coefficient of a sample point of the grid fingerprint database, a confidence coefficient of neighbor cell data of the grid fingerprint database and a confidence coefficient of the average level variance of the grid fingerprint database;

the second identification unit is used for identifying the movement type of the user and obtaining a confidence coefficient of the movement type of the user;

and the calculation unit is used for calculating the confidence coefficient of the grid geographic scene type, the confidence coefficient of the data type, the confidence coefficient of the cell number, the confidence coefficient of the sample point number, the confidence coefficient of the neighboring cell data, the confidence coefficient of the average level variance and the confidence coefficient of the movement type of the user by using a preset formula so as to obtain the confidence coefficient of the positioning result for positioning the user by using the measurement report.

Optionally, in the foregoing apparatus, the first identifying unit includes:

the first identification subunit is used for identifying the geographic scene type of the region where the grid is located as outdoor if more than seventy percent of the pixels of the grid are displayed outdoors, and the confidence coefficient of the geographic scene of the grid is a first grid scene confidence coefficient;

a second identifying subunit, configured to determine that the geographic scene type of the area where the grid is located is a building if more than seventy percent of pixels of the grid are displayed indoors, where the confidence coefficient of the grid geographic scene is a second grid scene confidence coefficient;

a third identifying subunit, configured to, if more than fifty percent of the pixels of the grid are displayed in a water area scene, determine that the geographic scene type of the area where the grid is located is a water area, and determine that the confidence coefficient of the grid geographic scene is a third grid scene confidence coefficient; wherein the first grid scene confidence coefficient is greater than the second grid scene confidence coefficient, which is greater than the third grid scene confidence coefficient.

Optionally, in the above apparatus, the analysis unit includes:

the first analysis subunit is used for determining that the confidence coefficient of the data type is a first data type confidence coefficient if the data type of the grid fingerprint database is sweep frequency data or road test data; if the data type of the grid fingerprint database is not sweep frequency data or road test data, the confidence coefficient of the data type is a second data type confidence coefficient; wherein the first data type confidence coefficient is greater than the second data type confidence coefficient;

a second analysis subunit, configured to determine that the confidence coefficient of the cell number is the first cell number confidence coefficient if any two serving cells in the grid fingerprint library can occupy more than ninety percent of the sample points; if no two serving cells in the grid fingerprint library can occupy more than ninety percent of the sample points, the grid fingerprint library cell number confidence coefficient is a second cell number confidence coefficient; wherein the first cell number confidence coefficient is greater than the second cell number confidence coefficient;

a third analysis subunit, configured to, if the number of sample points in the grid fingerprint database is greater than 20, determine that the confidence coefficient of the sample point is the first confidence coefficient of the sample point; if the number of the sample points in the grid fingerprint database is not more than 20, the confidence coefficient of the sample point is a second sample point confidence coefficient; wherein the first sample point confidence coefficient is greater than the second sample point confidence coefficient;

a fourth analyzing subunit, configured to determine, if the grid fingerprint library has data of a neighboring cell, that the confidence coefficient of the neighboring cell data is a first neighboring cell confidence coefficient, and if the grid fingerprint library does not have data of a neighboring cell, that the confidence coefficient of the neighboring cell data is a second neighboring cell confidence coefficient; the neighbor cell is another serving cell measured by the user in the measurement report except for the serving cell occupied by the user; the confidence coefficient of the first neighboring cell is greater than the confidence coefficient of the second neighboring cell;

a fifth analyzing subunit, configured to determine that the confidence coefficient of the average level variance is the first average level confidence coefficient if the level feature value variances of all sample points in the grid fingerprint library are smaller than 1; and if the level characteristic value variance of all sample points in the grid fingerprint database is not less than 1, the confidence coefficient of the average level variance is a second average level confidence coefficient, wherein the first average level confidence coefficient is greater than the second average level confidence coefficient.

Optionally, in the foregoing apparatus, the second identifying unit includes:

a fourth identifying subunit, configured to determine that the mobile type of the user is a mobile user and a confidence coefficient of the mobile type of the user is a first user mobile type confidence coefficient if the user calls for one time to occupy more than 3 serving cells and a maximum cell distance is more than 1 kilometer;

a fifth identifying subunit, configured to determine that the mobile type of the user is a stable user if the user only occupies 1 serving cell for one call service or the time of occupying one cell occupies more than eighty percent of the time of the call service, where a confidence coefficient of the mobile type of the user is a confidence coefficient of the mobile type of a second user;

a sixth identifying subunit, configured to, if it is identified that the movement type of the user is neither a mobile user nor a stable user, determine that the movement type of the user is a user of another type, and determine that the confidence coefficient of the movement type of the user is a confidence coefficient of a movement type of a third user; wherein the second user movement type confidence coefficient is greater than the first user movement type confidence coefficient, and the first user movement type confidence coefficient is greater than the third user movement type confidence coefficient.

Optionally, in the foregoing apparatus, the calculating unit includes:

the first calculating subunit is configured to sum the confidence coefficient of the data type, the confidence coefficient of the cell number, the confidence coefficient of the sample point number, the confidence coefficient of the neighboring cell data, and the confidence coefficient of the average level variance, and multiply by one fifth to obtain a first result value;

and the second calculating subunit is configured to multiply the first result value by the confidence coefficient of the grid geographic scene type and the confidence coefficient of the movement type of the user, so as to obtain a confidence of a positioning result obtained by positioning the user by using the measurement report.

Optionally, the above apparatus further includes:

a first determination unit, configured to determine that a positioning result of the user is accurate if a confidence of the positioning result obtained by positioning the user using the measurement report is higher than a preset threshold;

a second determining unit, configured to determine that the positioning result of the user is inaccurate if the confidence of the positioning result obtained by using the measurement report to position the user is not higher than a preset threshold.

According to the technical scheme, in the method for evaluating the confidence of the positioning result, when a user performs positioning by using the measurement report, the confidence coefficient of the grid geographic scene type, the confidence coefficient of the data type in the grid fingerprint database, the confidence coefficient of the cell number, the confidence coefficient of the sample point number, the confidence coefficient of the neighboring cell data, the confidence coefficient of the average level variance and the confidence coefficient of the movement type of the user are respectively obtained, and then the confidence of the positioning result of the user is calculated by using a preset formula. Therefore, the problem that an effective confidence evaluation cannot be performed on the positioning result of the user when the wireless MR of the user is used for positioning at present is solved.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.

Fig. 1 is a flowchart of a method for evaluating confidence of a positioning result according to an embodiment of the present disclosure;

FIG. 2 is a flowchart of one implementation of step S104 disclosed in another embodiment of the present application;

fig. 3 is a schematic diagram of a practical case of positioning by using a user MR according to another embodiment of the present application;

fig. 4 is a schematic diagram of an apparatus for evaluating confidence of a positioning result according to another embodiment of the present disclosure.

Detailed Description

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

In this application, 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 an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.

Moreover, in this document, relational terms such as first and second, and the like may be 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.

In the method for positioning the user based on the MR data, firstly, the MR data and the working parameter data of all users in the network are acquired through the existing network big data platform. The MR data mainly includes identification information of a serving cell and a neighboring cell where the user is located, and level measurement information. The working parameters are used for determining network positioning area information, wherein the information comprises longitude and latitude, direction angles, coverage scenes and the like of each cell in the base station. According to the region range and the grid dividing precision, the region is divided into a plurality of grids, and the size of each grid can be set to be 20m by 20 m. The grids may be numbered using the Loc-a-b-unit rule. Wherein a is the number of the abscissa and b is the number of the ordinate. For example, Loc-32-348-20 represents a grid with an abscissa 32, an ordinate 348 and a unit length of 20 meters.

Then, the data of the existing road test, the latitude and longitude data of the sweep frequency data and the area of the 20 × 20m grid are calculated, the results of the road test data and the sweep frequency data in the corresponding grid are calculated, the characteristics of the level of the service cell are statistically analyzed, and a grid fingerprint database is constructed, which can be divided into the following steps:

(1) and for any grid, analyzing the distribution conditions of the serving cell and the adjacent cells in the grid data, selecting the serving cell identifier with the most occurrence times as the grid serving cell identifier, and selecting the adjacent cell identifier with the most occurrence times as the adjacent cell identifier of the corresponding grid. Where the serving cell and neighbor cells may also be referred to as primary neighbor cells.

(2) And sequentially analyzing the level characteristic distribution condition of the main adjacent cell, fitting a data curve by adopting normal distribution, and using the level average value of the normal distribution as the level characteristic of the main adjacent cell.

(3) Constructing a fingerprint library of grids in the region by using the identification and level characteristics of main adjacent cells in the grids, and recording the fingerprint library as ((ECI)_i0，RSRP_i0)，(ECI_i1，RSRP_i1)...(ECI_in，RSRP_in) In which ECI)_inTo correspond toIdentity of the primary neighbour cell, RSRP_inAnd corresponding to the level characteristics of the main adjacent cell, i is the identifier of the grid in the area, and n is the number of data.

After the region grid fingerprint library is constructed, the user can be located using the user MR. When acquiring the data of the user MR to be positioned, matching the main adjacent cell identification in each grid aiming at the current main adjacent cell identification of the user in the user MR, and finding out the grid with the same main adjacent cell identification as a candidate grid. For each candidate trellis i, the MSE is calculated_i＝∑_n(rsrp_in-RSRP_in)²Wherein rsrp_inThe characteristic value of the level of the main adjacent cell of the current user. Computing MSE_iThe grid with the minimum value is the grid with the closest RSRP level, which is the same as the primary neighbor cell identifier in the MR data of the user to be positioned, and generally, the position of the grid is used as the positioning result of the user.

In order to make the positioning result of the user more accurate, after the best matching grid is obtained, the mobility type of the user can be determined by using S1-MME (transport Session Management (SM) and Mobility Management (MM) information, i.e. signaling plane or control plane information) interface data. The S1-MME interface data may provide, among other things, when the user occupies the primary neighbor cell list case. And constructing a network coverage cell list and a cell sequence of the urban road through a Geographic Information System (GIS) map of the city. And calculating whether the user has moving directivity and average speed according to the sequence condition that the user occupies different cells. If the vehicle moves in one direction within a certain time (for example, half an hour) and has a certain speed, the vehicle can be judged to move, otherwise, the vehicle is static. And then, correcting the user track according to the current geographic information and the user moving state to obtain the final positioning result of the user. For example, when the user moves in a mountain forest while driving, the longitude and latitude of the user are corrected to the nearest road, so that the positioning is further accurate. If the user state is static, the positioning result of the user is not corrected.

As can be seen from the background, currently, when the user performs the positioning by using the wireless MR, an effective confidence evaluation cannot be performed on the positioning result of the user. Therefore, in some cases where the accuracy of the positioning result of the user needs to be considered, a confidence level cannot be provided for the positioning result, and it cannot be determined whether the positioning result is accurate.

Based on this, the application provides a method and a device for evaluating confidence of measurement report positioning, so as to solve the problem that an effective confidence evaluation cannot be performed on a positioning result of a user when the user wireless MR is used for positioning at present.

The embodiment of the present application provides a measurement report positioning confidence evaluation method, as shown in fig. 1, specifically including:

s101, aiming at the grids matched during user positioning, identifying the geographic scene type of the area where the grids are located, and obtaining a confidence coefficient of the grid geographic scene type of the area where the grids are located; wherein, the user is a user who uses the measurement report to perform positioning.

It should be noted that, when the user is located by collecting the MR data of the user, the pixel distribution of the grid on the GIS map is checked by using the city GIS map for the grid matched during the user location, the geographic scene type of the area where the grid is located is identified, and the confidence coefficient of the grid geographic scene type of the area where the grid is located is obtained. The difficulty of positioning varies with different geographic scene types and scene complexity, and thus, the confidence coefficients corresponding to different grid geographic scene types vary.

Optionally, in another embodiment of the present application, an implementation manner of step S101 specifically includes:

if more than seventy percent of the pixels of the grid are displayed outdoors, the geographic scene type of the area where the grid is located is outdoors, and the confidence coefficient of the grid geographic scene is the first grid scene confidence coefficient.

If more than seventy percent of the pixels of the grid are displayed indoors, the geographic scene type of the area where the grid is located is a building, and the confidence coefficient of the grid geographic scene is the second grid scene confidence coefficient.

If more than fifty percent of the pixels of the grid are displayed in the water area scene, the geographic scene type of the area where the grid is located is the water area, and the confidence coefficient of the grid geographic scene is the confidence coefficient of the third grid scene; the first grid scene confidence coefficient is larger than the second grid scene confidence coefficient, and the second grid scene confidence coefficient is larger than the third grid scene confidence coefficient.

It should be noted that, in this embodiment, the smaller the positioning difficulty corresponding to each grid geographic scene type is, the larger the grid scene confidence coefficient may be set. The first grid scene confidence coefficient may be set to 1, the second grid scene confidence coefficient may be set to 0.7, and the third grid scene confidence coefficient may be set to 0.1.

S102, analyzing the grid fingerprint database corresponding to the grid to respectively obtain a confidence coefficient of the data type of the grid fingerprint database in the grid fingerprint database, a confidence coefficient of the cell number of the grid fingerprint database, a confidence coefficient of a sample point of the grid fingerprint database, a confidence coefficient of neighbor cell data of the grid fingerprint database and a confidence coefficient of the average level variance of the grid fingerprint database.

It should be noted that, because the data in the grid fingerprint database has a certain influence on the MR positioning accuracy if the data in the grid fingerprint database is not perfect and sufficient, the data in the grid fingerprint database also affects the confidence of the whole positioning result. Therefore, the grid fingerprint library of the grid matched in the user positioning result needs to be analyzed, wherein a confidence coefficient of a data type of the grid fingerprint library is obtained according to a data type used when the grid fingerprint library is constructed, a confidence coefficient of the number of cells of the grid fingerprint library is obtained according to the occupation ratio of each service cell occupying a sample point in the grid fingerprint library, a confidence coefficient of a sample point in the grid fingerprint library is obtained according to the number of the sample points in the grid fingerprint library, a confidence coefficient of neighbor cell data of the grid fingerprint library is obtained according to neighbor cell data in the grid fingerprint library, and a confidence coefficient of an average level variance of the grid fingerprint library is obtained according to level variance values of all the sample points in the grid fingerprint library.

Optionally, in another embodiment of the present application, an implementation manner of step S102 specifically includes:

if the data type of the grid fingerprint database is sweep frequency data or road test data, the confidence coefficient of the data type is a first data type confidence coefficient; if the data type of the grid fingerprint database is not the sweep frequency data or the road test data, the confidence coefficient of the data type is a second data type confidence coefficient; wherein the first data type confidence coefficient is greater than the second data type confidence coefficient.

It should be noted that, if the data in the grid fingerprint database is sweep frequency data or road test data, the accuracy of the data is higher, the confidence level is higher, and the confidence coefficient of the data type may be set to 1. If the data in the grid fingerprint library is not sweep data or road test data, the accuracy of the data is low, the confidence is low, and the confidence coefficient for the data type can be set to 0.8.

If any two serving cells can occupy more than ninety percent of the sample points in the grid fingerprint database, the confidence coefficient of the cell number is the first cell number confidence coefficient; if no two serving cells can occupy more than ninety percent of the sample points in the grid fingerprint database, the confidence coefficient of the grid fingerprint database cell number is the confidence coefficient of the second cell number; wherein the first cell number confidence coefficient is greater than the second cell number confidence coefficient.

It should be noted that, in the grid fingerprint database, if any two serving cells can occupy more than ninety percent of the sample points, the cell coverage of the grid is clear, the confidence is high, and the confidence coefficient of the number of cells can be set to 1. If no two cells can occupy more than ninety percent of the sample point, the cell coverage of the grid is complex, the confidence is low, and the confidence coefficient for the number of cells can be set to 0.8.

If the number of the sample points in the grid fingerprint database is more than 20, the confidence coefficient of the sample point is the first sample point confidence coefficient; if the number of the sample points in the grid fingerprint database is not more than 20, the confidence coefficient of the sample point is the confidence coefficient of the second sample point; wherein the first sample point confidence coefficient is greater than the second sample point confidence coefficient.

It should be noted that if the number of sample points in the grid fingerprint database is more than 20, which indicates that the sample data in the fingerprint database is sufficient, the confidence level is high, and the confidence coefficient of the sample points may be set to 1. If the number of sample points in the grid fingerprint database is not more than 20, which indicates that the sample data in the fingerprint database is insufficient, the confidence level is low, and the confidence coefficient of the sample points can be set to 0.8.

If the grid fingerprint database has the data of the adjacent cell, the confidence coefficient of the adjacent cell data is the confidence coefficient of the first adjacent cell, and if the grid fingerprint database does not have the data of the adjacent cell, the confidence coefficient of the adjacent cell data is the confidence coefficient of the second adjacent cell; the neighbor cells are other serving cells measured by the user in the measurement report except the serving cell occupied by the user; the confidence coefficient of the first neighboring cell is greater than the confidence coefficient of the second neighboring cell.

It should be noted that, if the grid fingerprint database has data of neighboring cells, the confidence is high, and the confidence coefficient of the data of neighboring cells may be set to 1. If the grid fingerprint database does not have the data of the neighbor cells, the confidence coefficient is low, and the confidence coefficient of the neighbor cell data can be set to be 0.7.

If the level characteristic value variance of all sample points in the grid fingerprint database is less than 1, the confidence coefficient of the average level variance is a first average level confidence coefficient; if the level characteristic value variance of all sample points in the grid fingerprint database is not less than 1, the confidence coefficient of the average level variance is a second average level confidence coefficient; wherein the first average level confidence coefficient is greater than the second average level confidence coefficient.

It should be noted that, if the variance of the level feature values of all the sample points in the grid fingerprint library is smaller than 1, which indicates that the level features of the sample points are relatively stable, the confidence is high, and the confidence coefficient of the average level variance may be set to 1. If the variance of the level feature values of all the sample points in the grid fingerprint library is not less than 1, which indicates that the level features of the sample points are not too stable, the confidence is low, and the confidence coefficient of the average level variance can be set to 0.7.

S103, identifying the movement type of the user, and obtaining a confidence coefficient of the movement type of the user.

It should be noted that, in the process of positioning, the difficulty of positioning may also be affected when the user is in different moving states, for example, when the user is moving or in a stationary state, and if the user is moving all the time, the difficulty of positioning may be greater than that in the stationary state. Therefore, it is necessary to identify the movement type of the user and obtain a confidence coefficient of the movement type of the user.

Optionally, in another embodiment of the present application, an implementation manner of step S103 specifically includes:

if the user once calls the service and occupies more than 3 service cells, and the maximum distance between the cells is more than 1 kilometer, the mobile type of the user is a mobile user, and the confidence coefficient of the mobile type of the user is the confidence coefficient of the mobile type of the first user.

It should be noted that, if a user occupies more than 3 serving cells for one call service and the maximum cell distance is more than 1 km, it may be determined that the mobile type of the user is a mobile user, and the confidence coefficient of the mobile type of the user may be set to 0.8.

If the user only occupies 1 service cell for one call service or occupies more than eighty percent of the time of one cell for the call service, the mobile type of the user is a stable user, and the confidence coefficient of the mobile type of the user is the confidence coefficient of the mobile type of the second user.

It should be noted that, if the user only occupies 1 serving cell for one call service, or the time occupied by one cell occupies more than eighty percent of the call service time, it may be determined that the mobile type of the user is a stable user, and the confidence coefficient of the mobile type of the user may be set to 1.

If the mobile type of the user is identified to be neither a mobile user nor a stable user, the mobile type of the user is the user of other types, and the confidence coefficient of the mobile type of the user is the confidence coefficient of the mobile type of the third user; the second user movement type confidence coefficient is greater than the first user movement type confidence coefficient, and the first user movement type confidence coefficient is greater than the third user movement type confidence coefficient.

It should be noted that, if it is recognized that the movement type of the user is neither a mobile user nor a stable user, the movement type of the user is determined to be another type of user, and the confidence coefficient of the movement type of the user may be set to 0.5.

S104, calculating a confidence coefficient of the grid geographic scene type, a confidence coefficient of the data type, a confidence coefficient of the cell number, a confidence coefficient of the sample point number, a confidence coefficient of the neighboring cell data, a confidence coefficient of the average level variance and a confidence coefficient of the movement type of the user by using a preset formula, and obtaining a confidence coefficient of a positioning result for positioning the user by using the measurement report.

It should be noted that after the confidence coefficient of the grid geographic scene type, the confidence coefficient of the data type, the confidence coefficient of the cell number, the confidence coefficient of the sample point number, the confidence coefficient of the neighboring cell data, the confidence coefficient of the average level variance, and the confidence coefficient of the movement type of the user are obtained, all the confidence coefficients are substituted into the confidence calculation formula for calculation, so as to obtain the confidence of the positioning result for positioning the user by using the measurement report. Therefore, when the user is positioned by using the MR data of the user, the confidence coefficient of each item of data is respectively calculated according to the data used by the positioning of the user, and finally a value related to the confidence of the positioning result is obtained and is synchronously output with the positioning result of the user, thereby providing an effective reference for the reliability of the positioning result of the user for related personnel.

Optionally, in another embodiment of the present application, as shown in fig. 2, an implementation manner of step S104 specifically includes:

s201, summing the confidence coefficient of the data type, the confidence coefficient of the cell number, the confidence coefficient of the sample point number, the confidence coefficient of the data of the adjacent cell and the confidence coefficient of the average level variance, and multiplying by one fifth to obtain a first result value.

S202, multiplying the first result value by the confidence coefficient of the grid geographic scene type and the confidence coefficient of the movement type of the user to obtain the confidence coefficient of the positioning result for positioning the user by using the measurement report.

It should be noted that, the formula for calculating the confidence of the positioning result may be set to K ═ F1+ F2+ F3+ F4+ F5)/5 × g × d, where F1 is the confidence coefficient of the data type, F2 is the confidence coefficient of the cell number, F3 is the confidence coefficient of the sample point number, F4 is the confidence coefficient of the neighboring cell data, F5 is the confidence coefficient of the average level variance, g is the confidence coefficient of the grid geographic scene type, and d is the confidence coefficient of the movement type of the user.

In the method for evaluating the confidence of the positioning result, when a user performs positioning by using a measurement report, a confidence coefficient of a grid geographic scene type, a confidence coefficient of a data type in a grid fingerprint database, a confidence coefficient of a cell number, a confidence coefficient of a sample point number, a confidence coefficient of adjacent cell data, a confidence coefficient of an average level variance and a confidence coefficient of a mobile type of the user are respectively obtained, and then the confidence of the positioning result of the user performing positioning by using the measurement report is calculated by using a preset formula. Therefore, the problem that an effective confidence evaluation cannot be performed on the positioning result of the user when the wireless MR of the user is used for positioning at present is solved.

Alternatively, after the confidence of the positioning result obtained by positioning the user by using the measurement report is obtained, one form of the confidence of the positioning result is output, which may be as shown in table 1:

TABLE 1

Optionally, in another embodiment of the present application, the method for evaluating the confidence of the positioning result may further include:

and if the confidence coefficient of the positioning result of the user positioning by using the measurement report is higher than a preset threshold value, judging that the positioning result of the user is accurate.

And if the confidence coefficient of the positioning result of the user positioning by using the measurement report is not higher than the preset threshold value, judging that the positioning result of the user is inaccurate.

It should be noted that in a practical case of positioning by using the user MR, in order to accurately reduce the coverage of the building, it is necessary to acquire the user measurement report in the area, and then perform the user positioning analysis. After positioning, 398845 MR measurement reports are acquired, wherein 312814 positioning points are located in the building, the proportion is 78.43%, and the result of coincidence of the positioning grids and the building is shown in FIG. 3. For the positioning data, the confidence distribution of the user positioning result and the data condition corresponding to the average level of the user measurement report are shown in table 2:

TABLE 2

The average level result of the field test in this area is-83.28, and it can be seen from table 2 that when the confidence reaches 75% and above 75%, the average level of the user measurement report is close to the average level result of the field test, so when the user locates the building area, the location result can be considered to be accurate when the confidence of the location result reaches 75% and above 75%.

Therefore, the preset threshold may be set to 75% in the embodiment of the present application, and if the confidence of the positioning result obtained by positioning the user by using the measurement report is higher than 75%, it is determined that the positioning result of the user is accurate. And if the confidence of the positioning result of positioning the user by using the measurement report is not higher than 75%, determining that the positioning result of the user is inaccurate.

The embodiment of the present application further provides an apparatus for evaluating a confidence of a positioning result, as shown in fig. 4, specifically including:

the first identification unit 401 is configured to identify, for a grid matched during user positioning, a geographic scene type of an area where the grid is located, and obtain a confidence coefficient of the geographic scene type of the area where the grid is located; wherein, the user is a user who uses the measurement report to perform positioning.

An analyzing unit 402, configured to analyze the grid fingerprint library corresponding to the grid, and obtain a confidence coefficient of a data type of the grid fingerprint library in the grid fingerprint library, a confidence coefficient of a cell number of the grid fingerprint library, a confidence coefficient of a sample point of the grid fingerprint library, a confidence coefficient of neighbor cell data of the grid fingerprint library, and a confidence coefficient of a mean level variance of the grid fingerprint library, respectively.

The second identifying unit 403 is configured to identify a movement type of the user and obtain a confidence coefficient of the movement type of the user.

The calculating unit 404 is configured to calculate, by using a preset formula, a confidence coefficient of a grid geographic scene type, a confidence coefficient of a data type, a confidence coefficient of a cell number, a confidence coefficient of a sample point number, a confidence coefficient of neighboring cell data, a confidence coefficient of an average level variance, and a confidence coefficient of a movement type of the user, so as to obtain a confidence of a positioning result that the user is positioned by using the measurement report.

In the device for evaluating the confidence of the positioning result, when a user performs positioning by using a measurement report, a confidence coefficient of a grid geographic scene type, a confidence coefficient of a data type in a grid fingerprint database, a confidence coefficient of a cell number, a confidence coefficient of a sample point number, a confidence coefficient of neighboring cell data, a confidence coefficient of an average level variance, and a confidence coefficient of a movement type of the user are obtained by a first identifying unit 401, an analyzing unit 402, and a second identifying unit 403, respectively, and then a confidence coefficient of a positioning result of positioning the user by using the measurement report is obtained by a calculating unit 404 by using a preset formula. Therefore, the problem that an effective confidence evaluation cannot be performed on the positioning result of the user when the wireless MR of the user is used for positioning at present is solved.

In this embodiment, the specific implementation processes of the first identifying unit 401, the analyzing unit 402, the second identifying unit 403, and the calculating unit 404 may refer to the contents of the method embodiment corresponding to fig. 1, and are not described herein again.

Optionally, in another embodiment of the present invention, an implementation manner of the first identifying unit 401 includes:

the first identification subunit is used for determining that the geographic scene type of the area where the grid is located is outdoor and the confidence coefficient of the grid geographic scene is the first grid scene confidence coefficient if more than seventy percent of the pixels of the grid are displayed outdoors.

And the second identification subunit is used for identifying the type of the geographic scene of the area where the grid is located as a building and the confidence coefficient of the grid geographic scene as the confidence coefficient of the second grid scene if more than seventy percent of the pixels of the grid are displayed indoors.

The third identification subunit is used for identifying the type of the geographical scene of the area where the grid is located as the water area if more than fifty percent of the pixels of the grid are displayed in the water area scene, and the confidence coefficient of the grid geographical scene is the confidence coefficient of the third grid scene; the first grid scene confidence coefficient is larger than the second grid scene confidence coefficient, and the second grid scene confidence coefficient is larger than the third grid scene confidence coefficient.

In this embodiment, the specific implementation processes of the first identifying subunit, the second identifying subunit and the third identifying subunit can refer to the contents of the above corresponding method embodiments, and are not described herein again.

Optionally, in another embodiment of the present invention, an implementation manner of the analysis unit 402 specifically includes:

the first analysis subunit is used for determining the confidence coefficient of the data type as the confidence coefficient of the first data type if the data type of the grid fingerprint database is sweep frequency data or road test data; if the data type of the grid fingerprint database is not the sweep frequency data or the road test data, the confidence coefficient of the data type is a second data type confidence coefficient; wherein the first data type confidence coefficient is greater than the second data type confidence coefficient.

The second analysis subunit is used for determining the confidence coefficient of the cell number to be the confidence coefficient of the first cell number if any two serving cells can occupy more than ninety percent of the sample points in the grid fingerprint database; if no two serving cells can occupy more than ninety percent of the sample points in the grid fingerprint database, the confidence coefficient of the grid fingerprint database cell number is the confidence coefficient of the second cell number; wherein the first cell number confidence coefficient is greater than the second cell number confidence coefficient.

The third analysis subunit is used for determining the confidence coefficient of the sample point as the confidence coefficient of the first sample point if the number of the sample points in the grid fingerprint database is more than 20; if the number of the sample points in the grid fingerprint database is not more than 20, the confidence coefficient of the sample point is the confidence coefficient of the second sample point; wherein the first sample point confidence coefficient is greater than the second sample point confidence coefficient.

The fourth analysis subunit is configured to, if the grid fingerprint library has data of a neighboring cell, determine the confidence coefficient of the neighboring cell data as a first neighboring cell confidence coefficient, and if the grid fingerprint library does not have data of a neighboring cell, determine the confidence coefficient of the neighboring cell data as a second neighboring cell confidence coefficient; the neighbor cells are other serving cells measured by the user in the measurement report except the serving cell occupied by the user; the confidence coefficient of the first neighboring cell is greater than the confidence coefficient of the second neighboring cell.

The fifth analysis subunit is configured to, if the level feature value variances of all the sample points in the grid fingerprint library are smaller than 1, determine that the confidence coefficient of the average level variance is the first average level confidence coefficient; and if the level characteristic value variance of all sample points in the grid fingerprint database is not less than 1, the confidence coefficient of the average level variance is a second average level confidence coefficient, wherein the first average level confidence coefficient is greater than the second average level confidence coefficient.

In this embodiment, for specific implementation processes of the first analysis subunit, the second analysis subunit, the third analysis subunit, the fourth analysis subunit, and the fifth analysis subunit, reference may be made to the contents of the corresponding method embodiments, and details are not described here.

Optionally, in another embodiment of the present invention, an implementation manner of the second identifying unit 403 specifically includes:

and the fourth identifying subunit is configured to, if the user occupies more than 3 serving cells for one call service and the maximum cell distance is more than 1 kilometer, determine that the mobile type of the user is a mobile user and the confidence coefficient of the mobile type of the user is the confidence coefficient of the mobile type of the first user.

And the fifth identifying subunit is used for determining that the mobile type of the user is a stable user and the confidence coefficient of the mobile type of the user is the confidence coefficient of the mobile type of the second user if the user only occupies 1 serving cell or occupies more than eighty percent of the time of one cell in the time of the call service.

A sixth identifying subunit, configured to, if it is identified that the movement type of the user is neither a mobile user nor a stable user, determine that the movement type of the user is a user of another type, and determine that the confidence coefficient of the movement type of the user is a confidence coefficient of a movement type of a third user; the second user movement type confidence coefficient is greater than the first user movement type confidence coefficient, and the first user movement type confidence coefficient is greater than the third user movement type confidence coefficient.

In this embodiment, for specific implementation processes of the fourth identifying subunit, the fifth identifying subunit and the sixth identifying subunit, reference may be made to the contents of the above corresponding method embodiments, and details are not described herein again.

Optionally, in another embodiment of the present invention, an implementation manner of the calculating unit 404 specifically includes:

and the first calculating subunit is used for summing the confidence coefficient of the data type, the confidence coefficient of the cell number, the confidence coefficient of the sample point number, the confidence coefficient of the neighboring cell data and the confidence coefficient of the average level variance, and then multiplying the sum by one fifth to obtain a first result value.

And the second calculating subunit is used for multiplying the first result value by the confidence coefficient of the grid geographic scene type and the confidence coefficient of the movement type of the user to obtain the confidence of the positioning result for positioning the user by using the measurement report.

In this embodiment, the specific execution processes of the first calculating subunit and the second calculating subunit can refer to the content of the method embodiment corresponding to fig. 2, and are not described herein again.

Optionally, in another embodiment of the present invention, the apparatus for evaluating the confidence of the positioning result may further include:

the first determination unit is configured to determine that the positioning result of the user is accurate if the confidence of the positioning result obtained by positioning the user using the measurement report is higher than a preset threshold.

And the second judging unit is used for judging that the positioning result of the user is inaccurate if the confidence coefficient of the positioning result of the user positioned by using the measurement report is not higher than a preset threshold value.

In this embodiment, for specific implementation processes of the first determining unit and the second determining unit, reference may be made to the contents of the corresponding method embodiments described above, and details are not described here again.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, the system or system embodiments are substantially similar to the method embodiments and therefore are described in a relatively simple manner, and reference may be made to some of the descriptions of the method embodiments for related points. The above-described system and system embodiments are only illustrative, wherein the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.

Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for evaluating confidence of a localization result, comprising:

2. The method of claim 1, wherein the identifying the geographic scene type of the area in which the grid is located and obtaining the confidence coefficient of the geographic scene type of the area in which the grid is located comprises:

3. The method according to claim 1, wherein the analyzing the grid fingerprint database corresponding to the grid to obtain a confidence coefficient of a data type of the grid fingerprint database in the grid fingerprint database, a confidence coefficient of a cell number of the grid fingerprint database, a confidence coefficient of a sample point of the grid fingerprint database, a confidence coefficient of neighbor data of the grid fingerprint database, and a confidence coefficient of a mean level variance of the grid fingerprint database respectively comprises:

4. The method of claim 1, wherein the identifying the type of movement of the user and obtaining a confidence coefficient for the type of movement of the user comprises:

5. The method according to claim 1, wherein the calculating, by using a preset formula, the confidence coefficient of the grid geographical scene type, the confidence coefficient of the data type, the confidence coefficient of the number of cells, the confidence coefficient of the number of sample points, the confidence coefficient of the neighboring cell data, the confidence coefficient of the average level variance, and the confidence coefficient of the movement type of the user to obtain the confidence of the positioning result for positioning the user by using the measurement report comprises:

6. The method of claim 1, wherein after obtaining the confidence level of the positioning result for positioning the user using the measurement report, further comprising:

7. An apparatus for evaluating confidence of a localization result, comprising:

8. The apparatus of claim 7, wherein the first identification unit comprises:

9. The apparatus of claim 7, wherein the analysis unit comprises:

10. The apparatus of claim 7, wherein the second identification unit comprises: