Parametric sparse representation for autofocused imaging through unknown walls

ABSTRACT

In order to obtain a high-resolution and well-focused image from compressively sampled echo data in the presence of wall ambiguity, a parametric sparse representation algorithm is proposed in this paper. A parametric dictionary with an unknown wall parameter is designed to represent the wall’s ambiguity. Then, imaging through unknown walls problem is converted into a joint optimization one which can be decomposed into sequential sparse imaging and wall parameter estimation. Specifically, the wall parameter estimation is performed by searching the maximum contrast. Numerical results are presented to demonstrate the validity and effectiveness of the proposed algorithm.

Acknowledgments

We want to thank the helpful comments and suggestions from the anonymous reviewers.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [Grant No. 61601242, 61601245]; State Key Laboratory of Millimeter Waves Open Project, Southeast University [Grant No. K201724]; Nanjing University of Posts and Telecommunications Foundation, China [Grant No. NY214047]; China Postdoctoral Science Foundation Funded Project [Grant No. 2016M601693].