Multi-view TWRI scene reconstruction using a joint Bayesian sparse approximation model

V. H. Tang; A. Bouzerdoum; S. L. Phung; F. H. C. Tivive

doi:10.1117/12.2180096

14 May 2015 Multi-view TWRI scene reconstruction using a joint Bayesian sparse approximation model

V. H. Tang, A. Bouzerdoum, S. L. Phung, F. H. C. Tivive

Proceedings Volume 9484, Compressive Sensing IV; 948405 (2015) https://doi.org/10.1117/12.2180096
Event: SPIE Sensing Technology + Applications, 2015, Baltimore, MD, United States

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.

Citation Download Citation

V. H. Tang, A. Bouzerdoum, S. L. Phung, and 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); https://doi.org/10.1117/12.2180096

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