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Proceedings Paper

Multi-view TWRI scene reconstruction using a joint Bayesian sparse approximation model
Author(s): V. H. Tang; A. Bouzerdoum; S. L. Phung; F. H. C. Tivive
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Paper 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.

Paper Details

Date Published: 14 May 2015
PDF: 10 pages
Proc. SPIE 9484, Compressive Sensing IV, 948405 (14 May 2015); doi: 10.1117/12.2180096
Show Author Affiliations
V. H. Tang, Univ. of Wollongong (Australia)
A. Bouzerdoum, Univ. of Wollongong (Australia)
S. L. Phung, Univ. of Wollongong (Australia)
F. H. C. Tivive, Univ. of Wollongong (Australia)


Published in SPIE Proceedings Vol. 9484:
Compressive Sensing IV
Fauzia Ahmad, Editor(s)

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