14 May 2015 Multi-view TWRI scene reconstruction using a joint Bayesian sparse approximation model
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Abstract
This paper addresses the problem of scene reconstruction in conjunction with wall-clutter mitigation for com- pressed multi-view through-the-wall radar imaging (TWRI). We consider the problem where the scene behind- the-wall is illuminated from different vantage points using a different set of frequencies at each antenna. First, a joint Bayesian sparse recovery model is employed to estimate the antenna signal coefficients simultaneously, by exploiting the sparsity and inter-signal correlations among antenna signals. Then, a subspace-projection technique is applied to suppress the signal coefficients related to the wall returns. Furthermore, a multi-task linear model is developed to relate the target coefficients to the image of the scene. The composite image is reconstructed using a joint Bayesian sparse framework, taking into account the inter-view dependencies. Experimental results are presented which demonstrate the effectiveness of the proposed approach for multi-view imaging of indoor scenes using a reduced set of measurements at each view.
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V. H. Tang, V. H. Tang, A. Bouzerdoum, A. Bouzerdoum, S. L. Phung, S. L. Phung, F. H. C. Tivive, F. H. C. Tivive, } "Multi-view TWRI scene reconstruction using a joint Bayesian sparse approximation model", Proc. SPIE 9484, Compressive Sensing IV, 948405 (14 May 2015); doi: 10.1117/12.2180096; https://doi.org/10.1117/12.2180096
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