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A Hermitian Positive Definite neural network for micro-Doppler complex covariance processing

Abstract : In its raw form, micro-Doppler radar data takes the form of a complex time-series, which can be seen as multiple realizations of a Gaussian process. As such, a complex covariance matrix constitutes a viable and synthetic representation of such data. In this paper, we introduce a neural network on Hermitian Positive Definite (HPD) matrices, that is complex-valued Symmetric Positive Definite (SPD) matrices, or complex covariance matrices. We validate this new architecture on synthetic data, comparing against previous similar methods.
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https://hal.sorbonne-universite.fr/hal-02422456
Contributor : Daniel Brooks <>
Submitted on : Sunday, December 22, 2019 - 12:51:49 PM
Last modification on : Tuesday, March 23, 2021 - 9:28:03 AM
Long-term archiving on: : Monday, March 23, 2020 - 1:18:29 PM

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  • HAL Id : hal-02422456, version 1

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Daniel Brooks, Olivier Schwander, Frédéric Barbaresco, Jean-Yves Schneider, Matthieu Cord. A Hermitian Positive Definite neural network for micro-Doppler complex covariance processing. International Radar Conference, Sep 2019, Toulon, France. ⟨hal-02422456⟩

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