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对ISAR成像的最小熵自聚焦(MEA)算法进行了收敛性分析.仿真结果表明,MEA算法存在局部最优问题,作为其代价函数的ISAR像熵函数并非多维补偿相位的下凸函数.只有当该补偿相位矢量的初值选取合适,使其处于像熵函数的全局最小点附近时,MEA算法才能收敛到全局最优解.针对MEA算法的最优化问题,给出了一种基于雷达成像的熵函数优化方法.该方法首先采用改进的多普勒中心跟踪法估计补偿相位初值.该初值是最大似然准则下的估计结果,可以使初始相位位于最优解附近.然后,利用快速MEA算法进行局部搜索,得到全局最优解.仿真结果表明,该算法不仅实现了MEA算法的全局最优求解,还可避免步长、阈值等参数的选择与调整.
The convergence analysis of minimum entropy auto-focusing (MEA) algorithm for ISAR imaging is given. The simulation results show that the MEA algorithm has a local optimal problem, and the ISAR entropy function as its cost function is not convex function of multi-phase compensation phase. When the initial value of the compensated phase vector is chosen to be close to the global minimum point of the entropy function, the MEA algorithm can converge to the global optimal solution. In view of the optimization problem of the MEA algorithm, a radar imaging based This method firstly uses the improved Doppler center tracking method to estimate the initial value of the compensation phase, which is the result of the estimation under the maximum likelihood criterion, so that the initial phase is located near the optimal solution. Then, MEA algorithm to obtain the global optimal solution.The simulation results show that this algorithm not only achieves the global optimal solution of the MEA algorithm but also avoids the selection and adjustment of parameters such as step size and threshold value.