| Citation: | Dongping YU, Yan GUO, Ning LI, Sixing YANG, Xiaoxiang SONG. Dictionary Refinement Method for Compressive Sensing Based Multi-target Device-free Localization[J]. Journal of Electronics & Information Technology, 2019, 41(4): 865-871. doi: 10.11999/JEIT180531
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In order to solve the dictionary mismatch problem of Compressive Sensing (CS) based multi-target Device-Free Localization (DFL) under the wireless localization environments, a Variational Expectation Maximization (VEM) based dictionary refinement method is proposed. Firstly, this method builds the dictionary based on the saddle surface model, and models the environment-related dictionary parameters as tunable parameters. Then, a two-layer hierarchical Gaussian prior model is imposed on the location vector to induce its sparsity. Finally, the VEM algorithm is adopted to estimate the posteriors of hidden variables and optimize the environment-related dictionary parameter, thus the estimation of target locations and dictionary refinement can be realized jointly. Compared with the conventional CS based multi-target DFL schemes, the simulation results demonstrate that the performance of the proposed algorithm is especially excellent in changing wireless localization environments.
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