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• W3179262760 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." @default.
• W3179262760 created "2021-07-19" @default.
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• W3179262760 date "2021-06-28" @default.
• W3179262760 modified "2023-10-16" @default.
• W3179262760 title "Robust Blind Source Separation by Soft Decision-Directed Non-Unitary Joint Diagonalization" @default.
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• W3179262760 doi "https://doi.org/10.48550/arxiv.2107.06170" @default.
• W3179262760 hasPublicationYear "2021" @default.
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