Majorize-Minimization Based Super-Resolution Method for Radar Forward-Looking Imaging

Sparse regularization method has been widely used to realize super-resolution imaging in radar forward-looking imaging. However, most of existed methods directly minimize a nondifferentiable L1 regularization problem. In this paper, a Majorize-Minimization (MM) based super-resolution method is proposed to realize super-resolution for radar forward-looking imaging. According to MM principle, the proposed method converts the non-differentiable L1 regularization problem into a differentiable L2 regularization problem, and the real target distribution is obtained by solving the L2 regularization problem. Due to the introduction of the sparse prior, the proposed method can better improve the azimuth resolution of radar forward-looking imaging. In addition, the application of MM principle makes the non-differentiable L1 regularization easier to be solved. Finally, the superior performance of the proposed method is verified by simulation.