Compressive sensing radar imaging with perturbations

Applying the theory of Compressive Sensing (CS) in radar always take kinds of perturbations into consideration, such as measurement noise, channel inference and radar system accuracy error. However, the performance of traditional Compressive Sensing Radar (CSR) is sensitivity to the above perturbations, which may make the performance of target information vector recovery degrade considerably. To solve the above problem, an iteration algorithm based on basis pursuit denoising (BPDN) and least-absolute shrinkage and selection operator (Lasso) is introduced. Firstly, the completely perturbed model of CSR is derived by considering the perturbations both in non-adaptive random measurement and sensing matrix. Secondly the corresponding optimization function is constructed. Since the above optimization problem is nonconvex, it is converted into two convex suboptimization functions to obtain the estimation of target information vector and perturbation vector. Simulations experimental results show that the proposed method performs better than classical CSR recovery approaches with smaller estimation error and better robustness against perturbation.