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

Conference Paper


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Abstract


  • This paper addresses the problem of scene reconstruction in conjunction with wall-clutter mitigation for compressed multi-view through-the-wall radar imaging (TWRI). We consider the problem where the scene behindthe-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.

Publication Date


  • 2015

Citation


  • V. Tang, A. Bouzerdoum, S. Lam. Phung & F. Tivive , "Multi-view TWRI scene reconstruction using a joint Bayesian sparse approximation model," in Conference on Compressive Sensing IV, 2015, pp. 948405-1-948405-10.

Scopus Eid


  • 2-s2.0-84943574052

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=5720&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/4699

Start Page


  • 948405-1

End Page


  • 948405-10

Abstract


  • This paper addresses the problem of scene reconstruction in conjunction with wall-clutter mitigation for compressed multi-view through-the-wall radar imaging (TWRI). We consider the problem where the scene behindthe-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.

Publication Date


  • 2015

Citation


  • V. Tang, A. Bouzerdoum, S. Lam. Phung & F. Tivive , "Multi-view TWRI scene reconstruction using a joint Bayesian sparse approximation model," in Conference on Compressive Sensing IV, 2015, pp. 948405-1-948405-10.

Scopus Eid


  • 2-s2.0-84943574052

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=5720&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/4699

Start Page


  • 948405-1

End Page


  • 948405-10