Deep unrolling of Robust PCA and Convolutional Sparse Coding for stationary target localization in through wall radar imaging - Laboratoire d’Informatique, Systèmes, Traitement de l’Information et de la Connaissance
Communication Dans Un Congrès Année : 2024

Deep unrolling of Robust PCA and Convolutional Sparse Coding for stationary target localization in through wall radar imaging

Résumé

Through Wall Radar Imaging aims to see through walls using electromagnetic waves. Low rank and sparse decomposition methods have been effective in processing returns in order to distinguish the wall response from the interior scene. However, they rely on model assumptions that can be a poor approximation of the actual physics. In the meantime, data-driven methods based on Deep Learning can provide an improvement regarding to this limitation. We thus propose a new unrolled network inspired by Robust PCA and Convolutional Sparse Coding which proves to be competitive and especially efficient in scarce data regimes.
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Dates et versions

hal-04707971 , version 1 (24-09-2024)

Identifiants

  • HAL Id : hal-04707971 , version 1

Citer

Hugo Brehier, Arnaud Breloy, Chengfang Ren, Guillaume Ginolhac. Deep unrolling of Robust PCA and Convolutional Sparse Coding for stationary target localization in through wall radar imaging. EUSIPCO 2024, Aug 2024, Lyon, France. ⟨hal-04707971⟩
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