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Efficient Estimation of Kronecker Product of Linear Structured Scatter Matrices under t-distribution

Abstract : This paper addresses structured scatter matrix estimation within the non convex set of Kronecker product structure. The latter model usually involves two matrices , which can be themselves linearly constrained, and arises in many applications, such as MIMO communication , MEG/EEG data analysis. Taking this prior knowledge into account generally improves estimation accuracy. In the framework of robust estimation, the t-distribution is particularly suited to model heavy-tailed data. In this context, we introduce an estimator of the scatter matrix, having a Kronecker product structure and potential linear structured factors. In addition, we show that the proposed method yields a consistent and efficient estimate.
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https://hal.archives-ouvertes.fr/hal-02873816
Contributor : Bruno Meriaux <>
Submitted on : Thursday, June 18, 2020 - 3:35:41 PM
Last modification on : Friday, October 23, 2020 - 4:30:03 PM

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Bruno Meriaux, Chengfang Ren, Arnaud Breloy, Mohammed Nabil El Korso, Philippe Forster. Efficient Estimation of Kronecker Product of Linear Structured Scatter Matrices under t-distribution. 28th European Signal Processing Conference (EUSIPCO 2020), Jan 2021, Amsterdam, Netherlands. ⟨hal-02873816⟩

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