Positioning method and system based on measurement report neighbor cell list

Abstract

The invention discloses a positioning method based on a measurement report neighbor cell list, and the method comprises the following steps: acquiring measurement report data of a mobile terminal; performing sample data acquisition and processing on the measurement report data to obtain a neighbor cell combination list and storing the neighbor cell combination list in a sample database; and searching the latitude and longitude information of the mobile terminal in the sample database. The invention further provides a positioning system based on the measurement report neighbor cell list, so the problems that the positioning error is large and the positioning result has an obvious geometric calculation trace are solved, a long-time background training process is not needed, and a sample library can be dynamically iterated and updated in real time; moreover, the positioning calculation can be carried out only through quick search, so that the positioning performance is high, server calculation resources required by positioning are greatly saved, and the method can be competent for real-time and quick positioning of massive measurement reports.

Description

technical field [0001] The invention relates to the field of communication technology, in particular to a positioning method and system based on a neighbor cell list of a measurement report. Background technique [0002] At present, the means of network optimization is mainly through geolocation of measurement reports, and its methods mainly include satellite positioning, triangulation calculation and fingerprint feature positioning. [0003] However, these three methods have obvious disadvantages: because the measurement of GPS signals has a certain power consumption, most users do not turn on the AGPS measurement switch when using mobile terminals, so this part of the data is difficult to represent the performance of all users in the network. The overall situation; triangulation is based on the measurement indicators in the measurement report, such as timing advance (TA) and reference signal received power (RSRP) in the 4G network, to estimate the distance between the term...

AI Technical Summary

Problems solved by technology

[0003] However, these three methods have obvious disadvantages: because the measurement of GPS signals has a certain power consumption, most users do not turn on the AGPS measurement switch when using mobile terminals, so this part of the data is difficult to represent the performance of all users in the network. The overall situation; triangulation is based on the measurement indicators in the measurement report, such as timing advance (TA) and reference signal received power (RSRP) in the 4G network, to estimate the distance between the terminal and the base station, and then based on the actual latitude and longitude of the base station , the terminal is positioned through geometric calculations, but the estimated distance value is not accurate enough, so the positioning error is large

In addition, the geographic location calculated by geometry is prone to arc-shaped and ray-shaped geometric calculation traces, and the rendering effect does not match the actual situation of the network. The disadvantage of fingerprint positioning is that the specific value of RSRP varies with different user terminal types and locations. Factors such as location (such as inside, indoor, and outdoor) and even weather influences fluctuate greatly, which may result in misjudgment when matching the sample library for search, resulting in large errors

On the other hand, in the training phase of the fingerprint feature sample library, all AGPS measurement report data needs to be summarized and counted by grid, and the real-time performance is also poor.

And in the positioning stage, it is necessary to calculate the fingerprint matching degree of each measurement report according to a large number of grids one by one, so the calculation amount is large, and it needs to occupy more server computing resources

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