| Citation: | HOU Ningning, LI Deng'ao, ZHAO Juminet al. Non-line-of-sight signal detection based on unsupervised learning and particle filtering[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(11): 2250-2258. doi: 10.13700/j.bh.1001-5965.2021.0077(in Chinese)
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Global navigation satellite system (GNSS) is the most widely used positioning technology at present. Due to high-rise structures blocking the signal, the performance deterioration caused on by non-line-of-sight propagation still remains while studying the positioning problem in urban canyons. In order to solve this problem, the unsupervised learning-partinle filter (UL-PF) algorithm is proposed. In the satellite signal classification stage, the unsupervised learning classification method using kernel k-means clustering is used. In the positioning stage, the particle filter method optimized by clustering algorithm is used. The method first considers the inherent similarity of the sampled particles in the state space distribution. Secondly, it explores how to select one particle as the key particle in each cluster and increase the diversity of resampled particle sets by using time series correlation techniques. Experiments show that the average positioning accuracy of the proposed algorithm in urban is improved from 15 m to about 5 m, and the convergence time is reduced from 500 s to about 200 s.
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