Robust Range Super-Resolution Imaging With Multicriteria Joint Constraints via Double Smoothed ${{l}_{0}}$-Norm Under Limited Resources

The radar's high-resolution range profile (HRRP) contains rich physical structural features of targets, making it of great value for target recognition and classification. However, in practical applications, the effective bandwidth of radar signals is limited due to the nonideal factors such as spectrum control, limited hardware equipment capabilities, and spectrum electromagnetic interference. Thus, it is difficult to reach the requirement of high-resolution and accurate target characterization in range profiles under limited resources, which will reduce subsequent detection, imaging, and recognition performance. To address the problem, this article proposes a robust range profile super-resolution method with multicriteria joint constraints of sparsity, low-rank, and Doppler under a multiple measurement vector model. In this technique, based on compressed sensing theory and adaptive filtering framework, a sparse reconstruction algorithm via double smoothed ${{l}_{0}}$-norm is proposed, which constructs a continuously differentiable Gaussian class function to approximate the ${{l}_{0}}$-norm. It combines accelerated gradient algorithm and zero attractor to optimize the multicriteria cost function and iteratively reconstruct the super-resolution range profile. The algorithm improves the super-resolution performance of range profiles, enhances the robustness against noise and complex range profile configurations, and can obtain high-precision HRRP of targets with limited radar resources. Extensive simulation and real data experiments demonstrate that it not only improves the average reconstruction accuracy of super-resolution range profile sequence, but effectively reduces the fluctuation of reconstruction errors. In addition, it does not require high azimuth resolution, and is also highly effective with a small number of sequence echoes.