一种用于密集强弱目标速度高分辨估计的IAA-MCapon算法

摘要

由于分辨精度有限以及易受目标能量强弱的影响，基于Fast Fourier Transform（FFT）的算法不能对位于同一距离单元的密集强弱目标进行有效的速度估计。基于此，本文采用基于协方差矩阵迭代自适应（Iterative Adaptive Algorithm，IAA）的改进Capon（Modified Capon，MCapon）算法对密集强弱目标速度参数进行高分辨估计。该方法首先采用Keystone变换进行距离走动校正，然后利用目标所在的距离单元数据进行协方差矩阵重构，接着利用MCapon方法使得密集强弱目标信号幅度输出均为常数1，最后实现了速度的高分辨估计，在保持高分辨的同时提高了稳健性。理论分析和实验仿真结果表明，所提方法可对包络校正后位于同一距离单元的密集强弱目标径向速度参数进行有效的高分辨估计，估计性能优于FFT类方法及子空间投影方法。%Due to the limited resolution and the effect of strong and weak targets,FFT-based algorithm cannot effec-tively estimate the velocities of dense strong and weak targets located in the same range gate.To deal with these issues,a modified Capon (MCapon)algorithm based on iterative adaptive algorithm (IAA)is proposed to achieve the high-resolu-tion velocity estimation of strong and weak targets with the close centers.The proposed algorithm first applies Keystone transform to correct the range walks of multiple moving targets,and then IAA is applied to obtain the reconstructed covari-ance matrix.After matrix eigenvalue decomposition,a MCapon detector is proposed to focus multiple targets,which keeps the outputs with the same amplitudes,i.e.,the constant 1. Finally,the high-resolution velocity estimation is achieved.There-fore,the proposed algorithm can significantly improve the resolution and robustness in velocity estimation of dense strong and weak targets.Simulated results validate the effectiveness of the proposed algorithm.