Indoor positioning method based on multipath energy fingerprints

Abstract

The invention provides an indoor positioning method based on multipath energy fingerprints, and belongs to the technical field of indoor wireless positioning and wireless communication. The method comprises the following steps that S1, in an off-line signal collecting stage, collected data is subjected to wireless signal estimation to obtain multipath parameters of k channels corresponding to each base station, in addition, RSSI (received signal strength indication) is calculated, and meanwhile, the corresponding position is measured to obtain a multi-dimension space fingerprint base; S2, in the on-line period, through actual measurement signals, the current position channel multipath parameters of users are obtained through estimation by using a channel estimation algorithm, in addition, the RSSI is calculated, and the actual measurement RSSI and the RSSI of the fingerprint base are compared to obtain fingerprint points with the 10 percent similarity; S3, the fingerprint points screened out in S2 are subjected to channel parameter comparison to obtain the average coordinate of several most similar points as the user position estimation value. The method provided by the invention has the advantages that the indoor channel multipath parameter information is perfectly merged into the indoor positioning algorithm, and the positioning precision and the stability can be more obviously improved.

Description

technical field [0001] The invention relates to the field of indoor wireless positioning technology and wireless communication technology. It mainly uses a fingerprint database indoor positioning algorithm and combines indoor channel multipath parameter estimation technology to provide an indoor positioning method based on multipath energy fingerprints. Background technique [0002] With the rapid development of data and multimedia services, people's demand for positioning and navigation is increasing, and various location-based services (LBS) have sprung up, such as navigation in shopping malls, exhibition halls, and transportation hubs. Location-based social network applications, remote factory management, assisted rescue in emergency situations, etc. With the rapid development of the mobile communication industry, the coverage of wireless signals in the indoor environment is also increasing, and indoor environment positioning is therefore possible. At the same time, MIMO...

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Problems solved by technology

[0004] However, these existing methods have more or less limitations: the first method has general positioning accuracy and low stability. The design of the fingerprint library and the transmission model is very simple, and the calculation time complexity is low. When using neural network and machine learning, if you want to obtain high precision, you have to use more complex algorithms, resulting in high computational complexity, and real-time performance may not be guaranteed; the second idea is mainly used in line-of-sight (LOS) ) scene, in the case of pure non-line-of-sight (NLOS), large geometric errors are caused by reflection and diffraction. In view of the fact that NLOS scenes are more common in the actual environment, its practicality is greatly reduced

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