Yuangang Zhang, Fangyuan He, Zhenzhen Liu, Xuqi Wang, and Wenqing Wang
Compressed sensing, personnel positioning, non-ranging, sparseadaptive matching pursuit
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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