A rank-deficient and sparse penalized optimization model for compressive indoor radar imaging

This paper proposes a rank-deficient and sparse penalized optimization method for addressing the problem of through-wall radar imaging (TWRI) in the presence of structured wall clutter. Compressive TWRI enables fast data collection and accurate target localization, but faces with the challenges of incomplete data measurements and strong wall clutter. This paper handles these challenges by formulating the task of wall-clutter removal and target image reconstruction as a joint rank-deficient and sparse regularized minimization problem. In this problem, the rank-deficient regularization is used to capture the low-dimensional structure of the wall signals and the sparse penalty is employed to represent the image of the desired targets. We develop a proximal gradient-based algorithm to solve the large-scale optimization problem, which simultaneously removes unwanted wall clutter and reconstruct an image of indoor targets. Real radar datasets are used to validate the effectiveness of the proposed rank-deficient and sparse regularized optimization approach. © 2019 IEEE.