针对现有参数稀疏恢复空时自适应处理中动目标参数估计方法在字典失配条件下性能下降的问题,提出一种基于原子范数无网格稀疏恢复技术的动目标参数估计方法,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估计,避免了基于固定离散字典模型进行参数稀疏恢复时遇到的字典失配问题,有效提高了动目标参数的估计性能。仿真结果证实了所提方法参数估计性能优于已有基于字典网格的稀疏恢复参数估计方法。

The performance of sparse recovery-based parameter estimation method for moving target in space-time adaptive processing degrades significantly in the case of dictionary mismatch, a gridless sparse recovery moving target parameter estimation method is proposed in this paper, which uses the technique of atomic norm to estimate space-time parameter of the moving target. According to continuous compressed sensing and low-rank property of the target covariance matrix, the estimation of azimuth and velocity for moving target is obtained with high accuracy, 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 based on fixed discrete dictionary, and thus improve the performance of parameter estimation effectively. Simulation results are performed to demonstrate that compared with the existing grid-based parameter estimation approaches for moving target, this approach has higher parameter estimation accuracy.

TN959.73;TN957.52

中央高校基本科研业务费中国民航大学资助专项(3122019048)