Off-grid compressive sensing through-the-wall radar imaging

Abstract

Compressive sensing techniques have been widely used to decrease the data acquisition time while generating high-resolution images due to the sparsity of the target space in through-the-wall radar imaging application. The CS-based imaging techniques mainly discretize the continuous target space into grid points and generate a dictionary of model data to form an optimization problem. The choice of the grid for generating the sparsity inducing basis or dictionary is a central point of CS and sparse approximation. However, good sparse recovery performance is based on the assumption that the targets are positioned at the pre-discretized grid locations; otherwise, the performance would significantly degrade. In this paper, the first-order approximation to estimate the targets' off-grid shifts and the joint sparse recovery method are used for reducing the effect of the grid to locate the off-grid target. Numerical examples demonstrate the robust results with lower localization errors using the joint sparse recovery method are obtained for off-grid targets compared to standard sparse reconstruction techniques.