A novel phase compensation method for ISAR imaging based on genetic algorithm

Phase compensation is a critical step in inverse synthetic aperture radar (ISAR) imaging. At present, the phase autofocus algorithm based on entropy minimization works well and is widely used in practice. However, due to the possible insufficient iteration time, the noise interference and other adverse factors, the compensation phases may be just a local optimization in solution space. In this paper, a novel phase compensation method for ISAR imaging based on genetic algorithm is proposed. Based on the phase autofocus result of entropy minimization method, the genetic algorithm is used to find the better local optimal solution or even global optimal solution. Genetic algorithm is a random search algorithm derived from the process of biological evolution, and it could be applied to a lot of optimization problems. According to the experimental results of real measured data, it is highly possible that the phase compensation effect of proposed method can exceed that of the entropy minimization method, which proves the effectiveness of our method.