Scene Recognition for Device-Free Indoor Localization

Abstract

Device-free indoor localization methods based on Channel State Information (CSI) have become an increasingly important topic. In complex indoor environments, both the line-of-sight (LOS) and non-LOS areas coexist, and the optimal parameters of the localization models for these two areas are different. In order to address this problem, in this paper, the scene-recognition indoor localization method is proposed to identify LOS and NLOS areas. First, the scene recognition model is given by combining mutation particle swarm optimization (MPSO) with backpropagation (BP) neural network. Then, the feature transformation method based on discriminant correlation analysis (DCA) is presented, which can effectively explore the correlation of amplitude data and phase data and form high-resolution location fingerprints, and further improve the indoor localization accuracy. Experimental results show that compared with the PAIL, LCAF, LSTM, BLS, PCNB, ABPS and FapFi algorithms, the proposed algorithm has higher localization accuracy.