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Article Dans Une Revue Signal Processing Année : 2022

Robust subspace tracking algorithms using fast adaptive Mahalanobis distance

Résumé

We consider the problem of robust subspace tracking (RST) in burst noise which appears in some applications in array signal processing and wireless communication. Our approach is based on robust adaptive covariance matrix, thus avoiding parameter fine-tuning in existing methods in the literature. However, this involves the estimation of the Mahalanobis distance, which has a quadratic computational complexity. We propose a novel algorithm to efficiently estimate the adaptive Mahalanobis distance, with linear complexity thanks to exploiting noise characteristics and the data structure. This approach can be used to robustify many existing RST algorithms. Particularly, in this paper, based on two efficient but non-robust algorithms, called YAST and LORAF, we propose their robust counterparts – RYAST and ROBUSTQR –, robust to burst noise while having the same complexity order as that of the original ones. We illustrate the effectiveness of the proposed RST algorithms by comparing them to the state-of-the-art in different scenarios.
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Dates et versions

hal-03589007 , version 1 (25-02-2022)

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Viet-Dung Nguyen, Nguyen Linh Trung, Karim Abed-Meraim. Robust subspace tracking algorithms using fast adaptive Mahalanobis distance. Signal Processing, 2022, 195, pp.108402. ⟨10.1016/j.sigpro.2021.108402⟩. ⟨hal-03589007⟩
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