非凸松弛原子范数空时动目标参数估计算法

Space-time moving target parameter estimation algorithm based on non-convex relaxation of atomic norm

针对参数稀疏恢复空时自适应处理中的动目标参数估计存在字典失配的问题,提出一种非凸松弛原子范数空时动目标参数估计算法。该方法利用目标回波在角度-多普勒域的稀疏特性,根据连续压缩感知和低秩矩阵恢复理论实现了运动目标方位角和速度的高精度、超分辨率估计,避免了稀疏恢复中的字典失配问题,有效提高了动目标参数估计性能。仿真实验结果表明,相较于已有基于字典网格的稀疏恢复参数估计方法和原子范数估计方法,所提算法具有更高的参数估计精度和对空间紧邻目标的分辨能力。

The performance of the sparse recovery-based parameter estimation methods for moving target in space-time adaptive processing(STAP)degrades significantly in the case of dictionary mismatch.A space-time moving target parameter estimation based on non-convex relaxation of atomic norm is proposed.According to continuous compressed sensing and low rank matrix recovery theorems,the estimation of azimuth and velocity for moving target are obtained with high accuracy and super resolution,which utilizes the intrinsically sparse characteristic of moving target echo in the angle-Doppler domain.The proposed method can avoid the dictionary mismatch problem in the sparse recovery,and improves the performance of parameter estimation effectively.Numerical results show that compared with the existing grid-based approaches and atomic norm minimization approach,this approach has higher estimation accuracy and allows closely spaced targets to be more easily distinguished.