COAL MINE TUNNEL PERSONNEL POSITIONING ALGORITHM BASED ON NON-RANGING COMPRESSED SENSING

Yuangang Zhang, Fangyuan He, Zhenzhen Liu, Xuqi Wang, and Wenqing Wang

Keywords

Compressed sensing, personnel positioning, nonranging, sparseadaptive matching pursuit

Abstract

The sparse nature of positioning in the spatial domain allows the use of compressed sensing theory for wireless positioning. Compressed sensing-based positioning algorithms can reduce the number of online measurements to a great degree and achieve high positioning accuracy at the same time, which makes compressed sensing-based positioning algorithms extremely attractive for tunnel positioning. However, traditional localization methods based on compressed sensing are mostly ranging and unsuitable for the energy-constrained low-loss wireless sensor network. Therefore, a coal mine tunnel personnel positioning algorithm based on non-ranging compressed sensing is proposed in this article. According to the connectivity information between the target nodes and the sensing nodes, the algorithm designs a non-ranging compressed sensing positioning model and establishes a database for the positioning area, which provides a solution to the problems of low positioning accuracy and time delay. Experiment and simulation results show that the proposed algorithm can achieve higher positioning accuracy and better robustness.

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