Space variant-based sparse regularization super-resolution imaging method for forward-looking scanning radar

Abstract

Regularization is an efficient technology for radar forward-looking imaging. Nevertheless, for high-speed moving platforms, the antenna pattern will normally be distorted, and therefore the imaging performance of the traditional regularization method will be seriously deteriorated. This paper proposes a space variant sparse regularization super-resolution imaging method for high-speed moving forward-looking scanning radar. By analyzing the time-variant relationship between the scanning angle and the sight angle, the pattern aberration is firstly analyzed. Then an efficient piecewise constant model is established for the sake of low computational complexity and restoring cost. Finally, the space variant sparse regularization method has been derived based on the aberrant model. Simulation experiments demonstrate that the proposed method can improve the super-resolution performance of the high-speed platforms more efficiently than the traditional regularization method.