一种基于最大似然估计的ISAR自聚焦算法

自聚焦是ISAR成像中的一项关键技术,其目的是消除目标和雷达的径向运动引起的相位误差。从ISAR信号模型出发,推导得到了相位误差的最大似然估计,并给出了一种基于最大似然估计的ISAR自聚焦算法。该算法采用了相位梯度算法(PGA)的处理结构,利用了多个距离单元上的散射点信号。算法中没有孤立散射点的要求,不需要相位解模糊,并且消除了相位误差估计中积累误差的影响,提高了相位误差的估计精度。将该算法应用于实测数据的ISAR成像中,得到了较好的聚焦结果。

An Autofocus Algorithm for ISAR Based on the Maximum Likelihood Estimation

As one of the key techniques in inverse synthetic radar(ISAR),autofocus is to eliminate the phase errors caused by the target radial motion.The maximum likelihood(ML) estimation of phase errors was derived form the ISAR signal model,and an autofocus algorithm based on the ML estimation was proposed.In the algorithm,the phase gradient algorithm(PGA) was performed,and the signals of multiple scatters on a few range bins were utilized.Hence,the scatters might not be well isolated,and the troublesome phase unwrapping was avoided.Moreover,the algorithm also eliminated the accumulation errors in the phase error estimation.The imaging results based on the measured ISAR data demonstrate a good performance of the autofocus algorithm.