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Wrapped Ambiguity Gaussian Mixed Model with Applications in Sparse Sampling based Multiple Parameter Estimation
Signal Processing ( IF 4.4 ) Pub Date : 2021-02-01 , DOI: 10.1016/j.sigpro.2020.107825 Hanshen Xiao , Nan Du , Zhikang Wang , Guoqiang Xiao
Signal Processing ( IF 4.4 ) Pub Date : 2021-02-01 , DOI: 10.1016/j.sigpro.2020.107825 Hanshen Xiao , Nan Du , Zhikang Wang , Guoqiang Xiao
Abstract Generalized Chinese Remainder Theorem (CRT) is a well-known approach to solve ambiguity resolution related problems. In this paper, we study the robust CRT reconstruction for multiple numbers from a view of statistics. To the best of our knowledge, it is the first rigorous analysis on the underlying statistical model of CRT-based multiple parameter estimation. Two novel approaches are established to deal with the problems of ambiguity resolution in parameter estimation. One is to directly calculate a conditional maximum a posteriori probability (MAP) estimation of the residue clustering, and the other is based on a generalized wrapped Gaussian mixture model to iteratively search for MAP of both estimands and clustering. Furthermore, residue error correcting codes are introduced to improve the robustness further. The experimental results show that the statistical schemes achieve much stronger robustness compared to state-of-the-art deterministic schemes, especially in heavy-noise scenarios.
更新日期：2021-02-01
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