Position fingerprint positioning method based on support vector machine

Abstract

The invention discloses a position fingerprint positioning method based on a support vector machine, and belongs to the field of wireless service supporting in mobile communication. The method mainly includes the two stages of offline training and online positioning. In the offline training stage, sample points are set, road test data are collected for the sample points, the road test data are trained based on a support vector machine algorithm, and then a positioning model is acquired. In the online positioning stage, after data of a mobile terminal to be positioned are acquired, quick positioning is achieved through the positioning model. By the adoption of the method, the positioning complexity of the online positioning stage can be effectively lowered, and positioning time delay is shortened.

Description

technical field [0001] The invention relates to the field of wireless service support in the mobile communication neighborhood, in particular to a position fingerprint positioning method based on a support vector machine. Background technique [0002] With the development of wireless communication, more and more people pay attention to wireless positioning technology. The existing popular positioning technologies include the following, Time of Arrival (ToA), Time Difference of Arrival (TDOA), Angle of Arrival (AOA), Global Positioning (GPS), and Location Fingerprinting (LFP). These positioning methods have their own advantages and disadvantages. However, with the development of the mobile Internet, the popularization of 3G mobile phones and the promotion of 4G mobile phones, high-precision positioning methods are not intended to be the key requirements of future mobile positioning technology. Among the above positioning methods, the fingerprint positioning method has the hi...

AI Technical Summary

Problems solved by technology

[0005] 1. It is necessary to conduct a global search on all historical data sets, which is computationally expensive and slow, making it difficult to meet the needs of real-time positioning and processing of massive user data

[0006] 2. Most of the kernel functions in support vector machines use Gaussian (radial basis) kernel functions or multi-order polynomial kernel functions, resulting in high computational complexity for classification decision functions

[0007] 3. The current classification decision strategy in the support vector machine method is to traverse all the different pairwise classification combinations, resulting in extremely high computational complexity for classification judgments

[0008] 4. Does not use multi-threading technology for accelerated optimization

[0009] In general, the existing localization methods based on support vector machines have high algorithm complexity, and the calculation speed cannot meet the actual needs.

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