A positioning method, device and storage medium

Abstract

The present invention discloses a positioning method, device and storage medium for use in the field of wireless communication technology, including: obtaining cell measurement data in LPP data; supplementing MDT data to the location information of the main cell and the neighboring cell in the LPP data; performing LPP positioning according to the cell measurement data and the location information of the main cell and the neighboring cell. The present invention can improve positioning accuracy and realize base station positioning accuracy improvement.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a positioning method, device and storage medium. Background Art

[0002] LPP (LTE Positioning Protocol; LTE: Long term evolution) is a general positioning communication protocol, and its main function is to transmit positioning assistance data and positioning information between the terminal and the network. In the 3GPP protocol, the content of LPP transmission data is defined, including: Cell, Wifi (Wireless Fidelity), Bluetooth, GNSS (Global Navigation Satellite System), etc. However, in actual use, it is subject to the support status of the terminal and the network, and often only Cell and wireless environment measurement reports (RSRP (Reference Signal Received Power), RSRQ (Reference Signal Received Quality), PCI (Physical Cell Identifier), FREQ (Frequency)) can be provided as the basis for positioning. Cell information is used as the main parameter to achieve positioning. Due to the single parameter, all existing E-CID (Enhanced Cell-ID positioning method) positioning technologies cannot apply LPP data.

[0003] Existing E-CID positioning technology mainly relies on two types of data: clock synchronization and signal transmission angle. The acquisition of these data requires support from mobile networks and terminals. In the current operator network environment, 2 / 4 / 5G networks cannot realize related functions and cannot provide these parameters.

[0004] Therefore, the deficiency of the prior art is that since the 2 / 4 / 5G network cannot provide parameters such as clock synchronization and signal transmission angle, the Cell ID positioning error is large when only using Cell information as the main parameter to achieve positioning. Summary of the invention

[0005] The present invention provides a positioning method, a device and a storage medium, which are used to solve the problem of large Cell ID positioning error.

[0006] The present invention provides the following technical solutions:

[0007] A positioning method, comprising:

[0008] Obtain cell measurement data in LPP data;

[0009] Supplement the MDT data to the location information of the primary cell and the neighboring cell in the LPP data;

[0010] LPP positioning is performed based on the cell measurement data and the location information of the primary cell and the neighboring cells.

[0011] During implementation, LPP positioning is performed based on cell measurement data and location information of the primary cell and neighboring cells, including:

[0012] LPP positioning is performed based on RSRP in the cell measurement data and the location information of the primary cell and the neighboring cells.

[0013] In implementation, when performing LPP positioning according to the cell measurement data and the location information of the primary cell and the neighboring cell, it also includes:

[0014] The LPP positioning method is adjusted using the weights obtained after training the MDT data.

[0015] In implementation, after obtaining the MDT data, the method further includes:

[0016] Get MDT data through the network;

[0017] Obtain the primary cell number, PCI and FREQ of the secondary cell provided in the MDT data;

[0018] According to the PCI and FREQ of the secondary cell and the location information of the primary cell, the PCI and FREQ are converted into a secondary cell number.

[0019] The implementation also includes:

[0020] Filter the acquired MDT data.

[0021] In implementation, the acquired MDT data is filtered as follows:

[0022] filtering out the MDT data of user terminals whose primary cell location serving the user terminal is more than a preset distance from the user terminal location; and / or,

[0023] The MDT data of the user terminals whose primary cell sampling data in the unit area is less than a preset number is filtered out based on the MDT data collection mechanism and the number of data sources.

[0024] In implementation, when obtaining weights after training the MDT data, it includes:

[0025] When training, use the following formula:

[0026] RSSI = A-10*n*log 10(d)

[0027] d=power(10,(A-RSSI) / (10*n))

[0028] Where power is the exponentiation, RSSI is the RSRP, and d is the distance;

[0029] Use multiple groups of MDT data for data training and obtain empirical values ​​of A and n as weights.

[0030] In implementation, the weights obtained after training the MDT data are used to adjust the LPP positioning method, including:

[0031] After determining the cell to be used for calculation, substitute the cell parameters into the following formula:

[0032] d=power(10,(A-RSSI) / (10*n))

[0033] Among them, power is the exponentiation, RSSI is the RSRP, d is the distance, and A and n are the weights obtained by training.

[0034] In implementation, the cell used for calculation is a cell selected according to an RSRP signal strength threshold, or a cell selected according to a distance threshold between a neighboring cell and a primary cell.

[0035] In implementation, LPP data is obtained through the E-SMLC protocol.

[0036] A positioning device, comprising:

[0037] The processor reads the program in the memory and performs the following processes:

[0038] Obtain cell measurement data in LPP data;

[0039] Supplement the MDT data to the location information of the primary cell and the neighboring cells in the LPP data;

[0040] Perform LPP positioning based on cell measurement data and location information of the primary cell and neighboring cells;

[0041] A transceiver is used to receive and send data under the control of the processor.

[0042] During implementation, LPP positioning is performed based on cell measurement data and location information of the primary cell and neighboring cells, including:

[0043] LPP positioning is performed based on RSRP in the cell measurement data and the location information of the primary cell and the neighboring cells.

[0044] In implementation, when performing LPP positioning based on cell measurement data and location information of the primary cell and the neighboring cell, it also includes:

[0045] The LPP positioning method is adjusted using the weights obtained after training the MDT data.

[0046] In implementation, after obtaining the MDT data, the method further includes:

[0047] Get MDT data through the network;

[0048] Obtain the primary cell number, PCI and FREQ of the secondary cell provided in the MDT data;

[0049] According to the PCI and FREQ of the secondary cell and the location information of the primary cell, the PCI and FREQ are converted into a secondary cell number.

[0050] The implementation also includes:

[0051] Filter the acquired MDT data.

[0052] In implementation, the acquired MDT data is filtered as follows:

[0053] filtering out the MDT data of user terminals whose primary cell location serving the user terminal is more than a preset distance from the user terminal location; and / or,

[0054] The MDT data of the user terminals whose primary cell sampling data in the unit area is less than a preset number is filtered out based on the MDT data collection mechanism and the number of data sources.

[0055] In implementation, when obtaining weights after training the MDT data, it includes:

[0056] When training, use the following formula:

[0057] RSSI = A-10*n*log10(d)

[0058] d=power(10,(A-RSSI) / (10*n))

[0059] Where power is the exponentiation, RSSI is the RSRP, and d is the distance;

[0060] Use multiple groups of MDT data for data training and obtain empirical values ​​of A and n as weights.

[0061] In implementation, the weights obtained after training the MDT data are used to adjust the LPP positioning method, including:

[0062] After determining the cell to be used for calculation, substitute the cell parameters into the following formula:

[0063] d=pewer(10,(A-RSSI) / (10*n))

[0064] Among them, power is the exponentiation, RSSI is the RSRP, d is the distance, and A and n are the weights obtained by training.

[0065] In implementation, the cell used for calculation is a cell selected according to an RSRP signal strength threshold, or a cell selected according to a distance threshold between a neighboring cell and a primary cell.

[0066] In implementation, LPP data is obtained through the E-SMLC protocol.

[0067] A positioning device, comprising:

[0068] An acquisition module, used to acquire cell measurement data in LPP data;

[0069] A supplementing module, used to supplement the MDT data to the location information of the primary cell and the neighboring cell in the LPP data;

[0070] The positioning module is used to perform LPP positioning according to the cell measurement data and the location information of the primary cell and the neighboring cells.

[0071] During implementation, the positioning module is further used to perform LPP positioning according to RSRP in the cell measurement data and the location information of the primary cell and the neighboring cells.

[0072] In implementation, the positioning module is further used to adjust the LPP positioning mode using the weights obtained after training the MDT data when performing LPP positioning according to the cell measurement data and the location information of the primary cell and the neighboring cells.

[0073] In implementation, the supplementary module is also used to obtain the MDT data through the network after obtaining the MDT data; obtain the primary cell number, PCI and FREQ of the secondary cell provided in the MDT data; and convert the PCI and FREQ into the secondary cell number according to the PCI and FREQ of the secondary cell and the location information of the primary cell.

[0074] During implementation, the supplementary module is also used to filter the acquired MDT data.

[0075] In implementation, the supplementary module is further used to filter the acquired MDT data in the following manner:

[0076] filtering out the MDT data of user terminals whose primary cell location serving the user terminal is more than a preset distance from the user terminal location; and / or,

[0077] Filter out the MDT data of user terminals whose sampled data of the serving cell in the unit area is lower than the preset quantity as judged based on the MDT data collection mechanism and the number of data sources.

[0078] During implementation, the supplementary module is further configured to, when obtaining weights after training the MDT data, include:

[0079] When performing training, use the following formula:

[0080] RSSI = A - 10 * n * log10(d)

[0081] d = power(10, (A - RSSI) / (10 * n))

[0082] Wherein, power is for exponentiation, RSSI is RSRP, and d is the distance;

[0083] Use multiple groups of MDT data for data training to obtain the empirical values of A and n as weights.

[0084] During implementation, the positioning module is further configured to adjust the LPP positioning method using the weights obtained after training the MDT data, including:

[0085] After determining the cell for calculation, substitute the cell parameters into the following formula:

[0086] d = power(10, (A - RSSI) / (10 * n))

[0087] Wherein, power is for exponentiation, RSSI is RSRP, d is the distance, and A and n are the weights obtained through training.

[0088] During implementation, the positioning module is further configured such that the cell for calculation is a cell selected according to the RSRP signal strength threshold or a cell selected according to the distance threshold between the neighboring cell and the serving cell.

[0089] During implementation, the acquisition module is further configured to obtain LPP data through the E - SMLC protocol.

[0090] A computer - readable storage medium stores a computer program for executing the above - mentioned positioning method.

[0091] The beneficial effects of the present invention are as follows:

[0092] When using Cell information as the main parameter to achieve positioning, due to the single parameter, all existing E-CID positioning technologies cannot apply LPP data. In the technical solution provided by the embodiment of the present invention, the characteristics of MDT and LPP in two different fields but the same source of necessary data are utilized, and MDT data is introduced. Since MDT data is supplemented to LPP data before LPP positioning is performed, the positioning accuracy can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0093] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0094] Figure 1 A schematic diagram of the implementation flow of the positioning method in an embodiment of the present invention;

[0095] Figure 2 Schematic diagram of the structure of the positioning device in an embodiment of the present invention. DETAILED DESCRIPTION

[0096] The technical solution provided in the embodiment of the present invention mainly solves the problem of large positioning error of existing Cell ID by relying on existing resources to improve positioning accuracy.

[0097] Figure 1 A flowchart for implementing the positioning method is shown in the figure, which may include:

[0098] Step 101: Obtain cell measurement data in LPP data;

[0099] Step 102: Supplement the MDT data to the location information of the primary cell and the neighboring cell in the LPP data;

[0100] Step 103: Perform LPP positioning according to the cell measurement data and the location information of the primary cell and the neighboring cells.

[0101] During implementation, LPP positioning is performed based on cell measurement data and location information of the primary cell and neighboring cells, including:

[0102] LPP positioning is performed based on RSRP in the cell measurement data and the location information of the primary cell and the neighboring cells.

[0103] In implementation, when performing LPP positioning based on cell measurement data and location information of the primary cell and the neighboring cell, it also includes:

[0104] The LPP positioning method is adjusted using the weights obtained after training the MDT data.

[0105] Specifically, the basic principle of MDT (Minimization Drive Test) is to optimize the network based on the terminal's wireless signal measurement report. To achieve this goal, the terminal must have the ability to measure the wireless environment (RSRP, RSRQ, PCI, FREQ), typical events, and location information. Traditional operators use MDT mainly to achieve automated network optimization and replace some manual tests.

[0106] The solution utilizes the characteristics of MDT and LPP, which are two different fields but have the same necessary data source. The mobile network wireless signal measurement data (ECI (Evolved Universal Terrestrial Radio Access Network Cell Identifier, E-UTRAN Cell Identifier), RSRP, RSRQ, PCI, FREQ) provided in the data are all terminal measurement reports. Using MDT data as the basic data source for data training is suitable for various positioning methods that need to obtain empirical value constants in mathematical models through data training. Among them, ECI = decimal ENB ID*256+decimal cell ID.

[0107] Data training is performed through MDT data to obtain reliable parameters. During the network positioning process, after the positioning engine obtains the base station data in LPP / LPPa (LPP refers to the positioning transmission protocol between UE and E-SMLC; LPPa refers to the positioning transmission protocol between eNB and E-SMLC) through E-SMLC (Evolution-Serving Mobile Location Centre), when using the positioning algorithm, the empirical value constants in the positioning algorithm obtained through data training are substituted into the formula to complete the positioning result calculation and improve the base station positioning accuracy.

[0108] The following will be divided into three parts: MDT data processing, data training using MDT data, and construction of a positioning method based on LPP, and then explained with examples.

[0109] (I) MDT data processing

[0110] In implementation, after obtaining the MDT data, the method further includes:

[0111] Get MDT data through the network;

[0112] Obtain the primary cell number, the PCI (physical cell identifier) ​​and FREQ (frequency) of the secondary cell provided in the MDT data;

[0113] According to the PCI and FREQ of the secondary cell and the location information of the primary cell, the PCI and FREQ are converted into a secondary cell number.

[0114] Specifically, it can be as follows:

[0115] 1: Obtain MDT data through the network and store the data in the database as basic data.

[0116] 2: Obtain the primary cell number, PCI and FREQ of the secondary cell provided in the MDT data. Convert the PCI and FREQ into the secondary cell number according to the PCI and FREQ and the location information of the primary cell.

[0117] 3: During the data acquisition process of MDT data, there may be data with inaccurate location information due to unstable terminal GPS (Global Positioning System) status. According to the actual situation, set data filtering conditions to clean up abnormal data. That is, in the specific implementation, it can also include: filtering the acquired MDT data.

[0118] (ii) Use MDT data for data training.

[0119] 1: Use MATLAB or other data analysis and deep learning software to build and write data training programs based on data models or principles.

[0120] Among them, MATLAB is a commercial mathematical software produced by MathWorks in the United States, which is used in data analysis, wireless communication, deep learning, image processing and computer vision, signal processing, quantitative finance and risk management, robotics, control systems and other fields. MATLAB is a combination of the two words matrix & laboratory, which means matrix factory or matrix laboratory.

[0121] 2: Obtain the empirical value constant in the positioning algorithm through data training.

[0122] (III) Construction of positioning method based on LPP.

[0123] 1: The interface can be developed in accordance with E-SMLC or other private protocols. That is, in practice, LPP data is obtained through the E-SMLC protocol.

[0124] 2: After obtaining the LPP request, convert it into the cell number of the secondary station according to PCI and FREQ. If the requester uses LPPa protocol data, the secondary station cell number can be directly provided, and the conversion step can be omitted.

[0125] 3: Use the positioning algorithm and the constants trained by MDT data to calculate the positioning result. Or calculate the intermediate variables that can be used to calculate the positioning result, and then use other algorithms to further complete the positioning result calculation.

[0126] The following is an implementation description using the logarithmic signal propagation model as an example:

[0127] (I) MDT data processing

[0128] 1: Get MDT data.

[0129] MDT data can be obtained through the network optimization system (ie, network optimization system) as basic training data. According to the MDT data specification, combined with actual usage, the data mainly includes measurement reports of 1 primary cell and up to 8 neighboring cells. The details can be as follows:

[0130] Cgi,TimeStamp,Cell ID, SCPCI, SCFreq, SCRSRP, SCRSRQ, Longitude, Latitude, NC1PCI, NC1Freq, NC1RSRP, NC1RSRQ, NC2PCI, NC2Freq, NC2RSRP, NC2RSRQ, NC3PCI, NC3Freq, NC3RSRP, NC3RSRQ, NC4PCI , NC4Freq, NC4RSRP, NC4RSRQ, NC5PCI, NC5Freq, NC5RSRP, NC5RSRQ, NC6PCI, NC6Freq, NC6RSRP, NC6RSRQ, NC7PCI, NC7Freq, NC7RSRP, NC7RSRQ, NC8PCI, NC8Freq, NC8RSRP, NC8RSRQ.

[0131] Among them, Cgi is Common Gateway Interface, TimeStamp is the timestamp, SC and NC are SC interface and NC interface respectively, for example, SCPCI, SCFreq, SCRSRP, SCRSRQ of the SC interface and NC1PCI, NC1Freq, NC1RSRP, NC1RSRQ of the NC interface, Longitude is longitude, and Latitude is latitude.

[0132] The data is used as basic training data and stored in the database.

[0133] 2: Generate a dimension table showing the relationship between PCI_Freq and Cell ID.

[0134] According to the correspondence between the primary cell fields in the MDT data: Cell ID, SCPCI, SCFreq, a PCI_Freq correspondence dimension table of the correspondence between PCI_Freq and Cell ID is generated. The same group of PCI_Freq may correspond to multiple CellIDs.

[0135] The PCI_Freq dimension table data is used to convert neighbor cell identifiers in MDT and LPP data. It can convert NCPCI and NCFreq in MDT neighbor cell measurement data into cell IDs. Or it can convert PCI and Freq in cell measurement data in LPP data into cell IDs. (This dimension table can also be obtained through other data resource management systems.)

[0136] 3: Get the cell ID location information dimension table.

[0137] The cell location information is obtained through field collection or through the integrated resource management system of the network management department, including at least: the cell unique identifier Cell ID, the location information of the base station to which the cell belongs (latitude Lat, longitude Lon), and the cell Cell ID location information dimension table is formed. A cell Cell ID corresponds to a unique longitude and latitude coordinate.

[0138] 4: Supplement the primary cell location information in the MDT data.

[0139] According to the Cell ID location information dimension table obtained in step 3, the location information (latitude Lat, longitude Lon) of the primary cell in the MDT data is supplemented for subsequent neighbor cell identity conversion and data training.

[0140] 5: Supplement the neighboring cell Cell ID number and location information in the MDT data.

[0141] Use the PCI_Freq dimension table generated in 2 to supplement the neighbor cell identifier CellID of the neighbor cell measurement data in the MDT data.

[0142] When PCI_Freq corresponds to a unique Cell ID, the unique Cell ID is used as the neighboring cell identifier, and the neighboring cell location information is supplemented according to the Cell ID location information dimension table generated in 3.

[0143] When PCI_Freq corresponds to multiple Cell IDs, obtain the location information (latitude Lat, longitude Lon) of multiple Cell IDs according to the Cell ID location information dimension table generated in 3, calculate the distance from the main cell location to the multiple Cell ID locations, take the Cell ID with the nearest distance as the neighboring cell converted by the PCI_Freq, and supplement the corresponding neighboring cell location information.

[0144] 6: Abnormal data filtering.

[0145] In implementation, the acquired MDT data may be filtered as follows:

[0146] filtering out the MDT data of user terminals whose primary cell location serving the user terminal is more than a preset distance from the user terminal location; and / or,

[0147] The MDT data of the user terminals whose primary cell sampling data in the unit area is less than a preset number is filtered out based on the MDT data collection mechanism and the number of data sources.

[0148] Specifically, during the data collection process of MDT data, there may be illegal data with inaccurate location information (latitude and longitude coordinates) collected due to unstable terminal GPS status. Usually, the following two methods can be used to filter illegal data:

[0149] The first type is that the location of the primary cell serving the user terminal should not exceed the rated distance from the user terminal location. The maximum distance of the reasonable coverage range of the primary cell is set to D according to the base station coverage density in the area where the primary cell is located. The distance between the user location and the primary cell location is calculated, and the data greater than the rated distance D is filtered out.

[0150] The second method is to judge based on the MDT data collection mechanism and the number of data sources. In a unit area, the sampling data of the primary cell should not be less than the rated number N. The MDT basic data coverage area (polygon surrounded by longitude and latitude) is divided into cells of rated size (for example, 50m*50m). The number of MDT data of each primary cell in each cell is calculated, and the data below the rated number N is filtered out.

[0151] After the above processing, the MDT basic data that can be used for data training is formed. The data content mainly includes:

[0152] User location (Longitude, Latitude), primary cell measurement data (Cell ID, Lat, Lon, RSRP), neighbor cell measurement data (Cell ID1, Lat1, Lon1, RSRP1, Cell ID2, Lat2, Lon2, RSRP2, Cell ID3, Lat3, Lon3, RSRP3, Cell ID4, Lat4, Lon4, RSRP4, Cell ID5, Lat5, Lon5, RSRP5, Cell ID6, Lat6, Lon6, RSRP6, Cell ID7, Lat7, Lon7, RSRP7, Cell ID8, Lat8, Lon8, RSRP8).

[0153] Among them, Longitude and Lon are longitude, Latitude and Lat are latitude, Cell ID is cell identifier, and RSRP is reference signal received power.

[0154] (ii) Use MDT data for data training.

[0155] In implementation, when obtaining weights after training the MDT data, it includes:

[0156] When training, use the following formula:

[0157] RSSI = A-10*n*log10(d)

[0158] d=power(10,(A-RSSI) / (10*n))

[0159] Where power is the exponentiation, RSSI is the RSRP, and d is the distance;

[0160] Use multiple groups of MDT data for data training and obtain empirical values ​​of A and n as weights.

[0161] The specific implementation can be as follows:

[0162] 1: Write a data training program.

[0163] Use MATLAB or other data analysis and deep learning software to write data training programs based on data models or principles.

[0164] Taking MATLAB as an example, use:

[0165] RSSI = A-10*n*log10(d)

[0166] d=power(10,(A-RSSI) / (10*n))

[0167] power: exponentiation, RSSI (Received Signal Strength Indication): RSRP.

[0168] 2: Use MDT basic data for data training.

[0169] After data training with multiple sets of data, empirical values ​​of A and n are obtained.

[0170] (3) Construction of positioning engine.

[0171] 1: Develop an interface to receive LPP positioning data.

[0172] According to the interface protocol of E-SMLC → positioning engine, an interface is developed to receive E-SMLC positioning requests and obtain cell measurement data in LPP data. The main contents include:

[0173] Primary cell measurement data (Cell ID, RSRP), neighbor cell measurement data (PCI1, Freq1, RSRP1, PCI2, Freq2, RSRP2, PCI3, Freq3, RSRP3...PCIn, Freqn, RSRPn).

[0174] 2: Supplement the location information of the primary and neighboring cells in the LPP data.

[0175] According to the PCI_Freq dimension table and the cell ID location information dimension table, the primary cell Cell ID in the LPP data is converted into location information. The neighboring cell PCI and Freq data in the LPP data are converted into Cell ID data, and the implementation method is the same as 1-5 in the MDT data processing. And the location information of the neighboring cell is supplemented.

[0176] If the data used by the E-SMLC to call the positioning engine is LPPa data, there is no need to perform the step of converting the PCI and Freq to the neighboring cell Cell ID.

[0177] 3: Calculate the distance based on the cell location and RSRP.

[0178] In implementation, the weights obtained after training the MDT data are used to adjust the LPP positioning method, including:

[0179] After determining the cell to be used for calculation, substitute the cell parameters into the following formula:

[0180] d=power(10,(A-RSSI) / (10*n))

[0181] Among them, power is the exponentiation, RSSI is the RSRP, d is the distance, and A and n are the weights obtained by training.

[0182] In a specific implementation, the cell used for calculation is a cell selected according to an RSRP signal strength threshold, or a cell selected according to a distance threshold between a neighboring cell and a primary cell.

[0183] Specifically, the cells used for calculation are screened according to the rules, and selection may be made based on RSRP signal strength or based on the distance between the neighboring cell and the primary cell.

[0184] Substitute the known parameters into the formula:

[0185] A and n are obtained through training, RSSI is RSRP,

[0186] d=power(10,(A-RSSI) / (10*n))

[0187] 4: Calculate the positioning results.

[0188] For example, the least squares method may be used to calculate the positioning result.

[0189] The side length calculation formula can be written as:

[0190]

[0191] In the formula, (X, Y) represents the coordinates of the point to be found, (x i ,y i ) represents the coordinates of the i-th base station, where i = 1, 2, 3, ... m; d i Represents the plane distance between the ith base station and the test point. Using the least squares principle, we can get:

[0192] Z=(X,Y)=(G T PG) _1 G T Ph

[0193] Where Z represents the coordinate estimate of the point to be found, and P represents the unit matrix.

[0194]

[0195]

[0196] The coordinates can be obtained according to the above formula to complete the final positioning.

[0197] Based on the same inventive concept, a positioning device and a computer-readable storage medium are also provided in an embodiment of the present invention. Since the principles of solving the problems by these devices are similar to those of the positioning method, the implementation of these devices can refer to the implementation of the method, and the repeated parts will not be repeated.

[0198] When implementing the technical solution provided by the embodiment of the present invention, it can be implemented as follows.

[0199] Figure 2 This is a schematic diagram of the positioning device structure. As shown in the figure, the device includes:

[0200] The processor 200 is used to read the program in the memory 220 and execute the following process:

[0201] Obtain cell measurement data in LPP data;

[0202] Supplement the MDT data to the location information of the primary cell and the neighboring cells in the LPP data;

[0203] Perform LPP positioning based on cell measurement data and location information of the primary cell and neighboring cells;

[0204] The transceiver 210 is configured to receive and send data under the control of the processor 200 .

[0205] During implementation, LPP positioning is performed based on cell measurement data and location information of the primary cell and neighboring cells, including:

[0206] LPP positioning is performed based on RSRP in the cell measurement data and the location information of the primary cell and the neighboring cells.

[0207] In implementation, when performing LPP positioning according to the cell measurement data and the location information of the primary cell and the neighboring cell, it also includes:

[0208] The LPP positioning method is adjusted using the weights obtained after training the MDT data.

[0209] In implementation, after obtaining the MDT data, the method further includes:

[0210] Get MDT data through the network:

[0211] Obtain the primary cell number, PCI and FREQ of the secondary cell provided in the MDT data;

[0212] According to the PCI and FREQ of the secondary cell and the location information of the primary cell, the PCI and FREQ are converted into a secondary cell number.

[0213] The implementation also includes:

[0214] Filter the acquired MDT data.

[0215] In implementation, the acquired MDT data is filtered as follows:

[0216] filtering out the MDT data of user terminals whose primary cell location serving the user terminal is more than a preset distance from the user terminal location; and / or,

[0217] The MDT data of the user terminals whose primary cell sampling data in the unit area is less than a preset number is filtered out based on the MDT data collection mechanism and the number of data sources.

[0218] In implementation, when obtaining weights after training the MDT data, it includes:

[0219] When training, use the following formula:

[0220] RSSI = A-10*n*log10(d)

[0221] d=power(10,(A-RSSI) / (10*n))

[0222] Where power is the exponentiation, RSSI is the RSRP, and d is the distance;

[0223] Use multiple groups of MDT data for data training and obtain empirical values ​​of A and n as weights.

[0224] In implementation, the weights obtained after training the MDT data are used to adjust the LPP positioning method, including:

[0225] After determining the cell to be used for calculation, substitute the cell parameters into the following formula:

[0226] d=pewer(10,(A-RSSI) / (10*n))

[0227] Among them, power is the exponentiation, RSSI is the RSRP, d is the distance, and A and n are the weights obtained by training.

[0228] In implementation, the cell used for calculation is a cell selected according to an RSRP signal strength threshold, or a cell selected according to a distance threshold between a neighboring cell and a primary cell.

[0229] In implementation, LPP data is obtained through the E-SMLC protocol.

[0230] Among them, Figure 2 In the embodiment, the bus architecture may include any number of interconnected buses and bridges, specifically linking together various circuits of one or more processors represented by processor 200 and memory represented by memory 220. The bus architecture may also link together various other circuits such as peripherals, voltage regulators, and power management circuits, which are well known in the art and are therefore not further described herein. The bus interface provides an interface. The transceiver 210 may be a plurality of components, namely, a transmitter and a receiver, providing a unit for communicating with various other devices on a transmission medium. The processor 200 is responsible for managing the bus architecture and general processing, and the memory 220 may store data used by the processor 200 when performing operations.

[0231] An embodiment of the present invention further provides a positioning device, including:

[0232] An acquisition module, used to acquire cell measurement data in LPP data;

[0233] A supplementing module, used to supplement the MDT data to the location information of the primary cell and the neighboring cell in the LPP data;

[0234] The positioning module is used to perform LPP positioning according to the cell measurement data and the location information of the primary cell and the neighboring cells.

[0235] During implementation, the positioning module is further used to perform LPP positioning according to RSRP in the cell measurement data and the location information of the primary cell and the neighboring cells.

[0236] In implementation, the positioning module is further used to adjust the LPP positioning mode using the weights obtained after training the MDT data when performing LPP positioning according to the cell measurement data and the location information of the primary cell and the neighboring cells.

[0237] In implementation, the supplementary module is also used to obtain the MDT data through the network after obtaining the MDT data; obtain the primary cell number, PCI and FREQ of the secondary cell provided in the MDT data; and convert the PCI and FREQ into the secondary cell number according to the PCI and FREQ of the secondary cell and the location information of the primary cell.

[0238] During implementation, the supplementary module is also used to filter the acquired MDT data.

[0239] In implementation, the supplementary module is further used to filter the acquired MDT data in the following manner:

[0240] filtering out the MDT data of user terminals whose primary cell location serving the user terminal is more than a preset distance from the user terminal location; and / or,

[0241] The MDT data of the user terminals whose primary cell sampling data in the unit area is less than a preset number is filtered out based on the MDT data collection mechanism and the number of data sources.

[0242] In implementation, the supplementary module is also used to obtain weights after training the MDT data, including:

[0243] When training, use the following formula:

[0244] RSSI = A-10*n*log10(d)

[0245] d=power(10,(A-RSSI) / (10*n))

[0246] Where power is the exponentiation, RSSI is the RSRP, and d is the distance;

[0247] Use multiple groups of MDT data for data training and obtain empirical values ​​of A and n as weights.

[0248] During implementation, the positioning module is further used to adjust the LPP positioning method using the weights obtained after training the MDT data, including:

[0249] After determining the cell to be used for calculation, substitute the cell parameters into the following formula:

[0250] d=power(10,(A-RSSI) / (10*n))

[0251] Among them, power is the exponentiation, RSSI is the RSRP, d is the distance, and A and n are the weights obtained by training.

[0252] In implementation, the positioning module is further used to determine whether the cell used for calculation is a cell selected according to an RSRP signal strength threshold, or a cell selected according to a distance threshold between a neighboring cell and a primary cell.

[0253] During implementation, the acquisition module is also used to acquire LPP data through the E-SMLC protocol.

[0254] For the convenience of description, the various parts of the above-mentioned device are divided into various modules or units according to their functions and described separately. Of course, when implementing the present invention, the functions of each module or unit can be implemented in the same or multiple software or hardware.

[0255] A computer-readable storage medium is also provided in an embodiment of the present invention, wherein the computer-readable storage medium stores a computer program for executing the above positioning method.

[0256] For specific implementation, please refer to the implementation of the positioning method.

[0257] In summary, in the technical solution provided in the embodiment of the present invention, a solution is proposed for improving the positioning accuracy of base station data in LPP data by using MDT data.

[0258] The method of using the wireless signal measurement data such as Eci, RSRP, PCI, FREQ in the MDT data for training and applying the training results to the LPP data for positioning calculation is the core of the present invention and should be protected.

[0259] Specifically, compared with the traditional E-CID positioning technology, this solution has at least one of the following characteristics:

[0260] Can effectively improve positioning accuracy: Affected by the deployment density of operator base stations in the region, the positioning accuracy improvement effect is better in low-density areas.

[0261] It has low reliance on networks and terminals and can be implemented based on the operator's existing network. However, other TA and AOA E-CID positioning technologies have high reliance on networks and terminals.

[0262] It is highly robust and can be used for positioning in all environments. It does not require modification of existing equipment and has good universality.

[0263] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program code.

[0264] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0265] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0266] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0267] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A positioning method, It is characterized in that include: Obtain cell measurement data in Long Term Evolution Positioning Protocol LPP data; Supplementing the minimization of drive tests (MDT) data to the location information of the primary cell and the neighboring cell in the LPP data; LPP positioning is performed based on the cell measurement data and the location information of the primary cell and the neighboring cells.

2. The method according to claim 1, It is characterized in that LPP positioning is performed based on the cell measurement data and the location information of the primary cell and neighboring cells, including: LPP positioning is performed based on the reference signal received power RSRP in the cell measurement data and the location information of the primary cell and the neighboring cells.

3. The method according to claim 1, It is characterized in that When performing LPP positioning based on cell measurement data and location information of the primary cell and neighboring cells, it also includes: The LPP positioning method is adjusted using the weights obtained after training the MDT data.

4. The method according to claim 3, It is characterized in that After acquiring the MDT data, the method further includes: Get MDT data through the network; Obtain the primary cell number, the physical cell identifier PCI and the frequency FREQ of the secondary cell provided in the MDT data; According to the PCI and FREQ of the secondary cell and the location information of the primary cell, the PCI and FREQ are converted into a secondary cell number.

5. The method according to claim 4, It is characterized in that Also includes: Filter the acquired MDT data as follows: filtering out the MDT data of user terminals whose primary cell location serving the user terminal is more than a preset distance from the user terminal location; and / or, The MDT data of the user terminals whose primary cell sampling data in the unit area is less than a preset number is filtered out based on the MDT data collection mechanism and the number of data sources.

6. The method according to claim 3, It is characterized in that When obtaining weights after training the MDT data, it includes: When training, use the following formula: RSSI = A-10*n*log 10(d) d=power(10,(A-RSSI) / (10*n)) Where power is the exponentiation, received signal strength indication RSSI is RSRP, and d is the distance; Use multiple groups of MDT data for data training and obtain empirical values ​​of A and n as weights.

7. The method according to claim 3, It is characterized in that The weights obtained after training the MDT data are used to adjust the LPP positioning method, including: After determining the cell to be used for calculation, substitute the cell parameters into the following formula: d=power(10,(A-RSSI) / (10*n)) Among them, power is the exponentiation, RSSI is the RSRP, d is the distance, and A and n are the weights obtained by training.

8. The method according to claim 7, It is characterized in that The cell used for calculation is a cell selected according to the RSRP signal strength threshold, or a cell selected according to the distance threshold between the neighboring cell and the primary cell.

9. The method according to claim 1, It is characterized in that LPP data is obtained through the Evolved Serving Mobile Location Center E-SMLC protocol.

10. A positioning device, It is characterized in that include: The processor reads the program in the memory and performs the following processes: Obtain cell measurement data in LPP data; Supplement the MDT data to the location information of the primary cell and the neighboring cell in the LPP data; Perform LPP positioning based on cell measurement data and location information of the primary cell and neighboring cells; A transceiver is used to receive and send data under the control of the processor.

11. A positioning device, It is characterized in that include: An acquisition module, used to acquire cell measurement data in LPP data; A supplementing module, used to supplement the MDT data to the location information of the primary cell and the neighboring cell in the LPP data; The positioning module is used to perform LPP positioning according to the cell measurement data and the location information of the primary cell and the neighboring cells.

12. A computer-readable storage medium, It is characterized in that The computer-readable storage medium stores a computer program for executing the method according to any one of claims 1 to 9.

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