Multi-view indoor scene reconstruction from compressed through-wall radar measurements using a joint Bayesian sparse representation
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 the sparsity and correlations between antenna signals. Following joint signal coefficient estimation, a subspace projection technique is applied to segregate the target coefficients from the wall contributions. Furthermore, a multitask linear model is developed to relate the target coefficients to the scene, and a composite scene image is reconstructed by a joint Bayesian sparse framework, taking into account the inter-view dependencies. Experimental results show that the proposed approach improves reconstruction accuracy and produces a composite scene image in which the targets are enhanced and the background clutter is attenuated.
V. Tang, A. Bouzerdoum, S. L. Phung & F. Tivive , "Multi-view indoor scene reconstruction from compressed through-wall radar measurements using a joint Bayesian sparse representation," in Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on, 2015, pp. 2419-2423.