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 non-differentiable L-1 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 L-1 regularization problem into a differentiable L-2 regularization problem, and the real target distribution is obtained by solving the L-2 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 L-1 regularization easier to be solved. Finally, the superior performance of the proposed method is verified by simulation.