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This paper presents a low-rank and jointly-sparse approach for imaging stationary targets using multipolarization through-wall radar (TWR). The proposed approach exploits two important characteristics of multichannel TWR signals: low-rank structure of the wall reflections and jointly-sparse structure of the polarization images. The task of removing wall reflections and reconstructing multichannel...
This paper addresses the problem of indoor scene reconstruction in compressed sensing through-the-wall radar imaging. The proposed method is motivated by two observations that wall reflections reside in a low-rank subspace and the imaged scene tends to be sparse. The task of mitigating the wall reflections and reconstructing an image of the scene behind-the-wall is cast as a joint low-rank and sparsity...
This paper introduces a joint low-rank and sparsity-based model to address the problem of wall-clutter mitigation in compressed through-the-wall radar imaging. The proposed model is motivated by two observations that wall reflections reside in a low-rank subspace, and target signals tend to be sparse. In the proposed approach, the task of segregating target returns from wall reflections is formulated...
This paper addresses the problem of scene reconstruction, incorporating wall-clutter mitigation, for compressed multi-view through-the-wall radar imaging. We consider the problem where the scene is sensed using different reduced sets of frequencies at different antennas. A joint Bayesian sparse recovery framework is first employed to estimate the antenna signal coefficients simultaneously, by exploiting...
This paper addresses the problem of wall clutter mitigation in compressed sensing through-the-wall radar imaging, where a different set of frequencies is sensed at different antenna locations. A joint Bayesian sparse approximation framework is first employed to reconstruct all the signals simultaneously by exploiting signal sparsity and correlations between antenna signals. This is in contrast to...
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