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arXiv · 2107.06170

Robust Blind Source Separation by Soft Decision-Directed Non-Unitary Joint Diagonalization

Wenjuan Liu·Dazheng Feng·Bingnan Pei·Mengdao Xing·Xinhong Meng·Qianru Wei
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Abstract

Approximate joint diagonalization of a set of matrices provides a powerful framework for numerous statistical signal processing applications. For non-unitary joint diagonalization (NUJD) based on the least-squares (LS) criterion, outliers, also referred to as anomaly or discordant observations, have a negative influence on the performance, since squaring the residuals magnifies the effects of them. To solve this problem, we propose a novel cost function that incorporates the soft decision-directed scheme into the least-squares algorithm and develops an efficient algorithm. The influence of the outliers is mitigated by applying decision-directed weights which are associated with the residual error at each iterative step. Specifically, the mixing matrix is estimated by a modified stationary point method, in which the updating direction is determined based on the linear approximation to the gradient function. Simulation results demonstrate that the proposed algorithm outperforms conventional non-unitary diagonalization algorithms in terms of both convergence performance and robustness to outliers.

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Wenjuan Liu, Dazheng Feng, Bingnan Pei, Mengdao Xing, Xinhong Meng, Qianru Wei. 2021-06-28. Robust Blind Source Separation by Soft Decision-Directed Non-Unitary Joint Diagonalization. https://arxiv.org/abs/2107.06170

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