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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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Dates and versions

hal-02422456 , version 1 (22-12-2019)

Identifiers

  • HAL Id : hal-02422456 , version 1

Cite

Daniel A 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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