基于原子范数的无网格稀疏恢复非正侧视阵空时自适应处理算法

基于杂波谱稀疏恢复的空时自适应处理(STAP)方法可以显著降低对杂波样本数的要求，十分适合缺少样本情况下的机载雷达杂波抑制。然而，现有稀疏恢复STAP方法利用离散化空时导向矢量字典进行重构，在非正侧视阵情况下，由于杂波脊不在字典网格点上，字典失配问题严重影响杂波抑制性能。针对上述问题，该文提出了一种基于原子范数的无网格稀疏恢复空时自适应处理方法(ANM-STAP)，利用低秩矩阵恢复理论实现连续空时平面的稀疏恢复，克服了稀疏恢复中的字典失配问题，获得了非正侧视阵情况下的高分辨率杂波空时谱，有效提高了STAP杂波抑制性能。Monte Carlo实验证明，该文方法STAP处理性能在非正侧视阵情况下优于已有字典离散化处理的稀疏恢复STAP方法。

Gridless Sparse Recovery for Non-sidelooking Space-Time Adaptive Processing Based on Atomic Norm Minimization

Sparse recovery Space-Time Adaptive Processing (STAP) can reduce the requirements of clutter samples, and suppress effectively clutter using limited training samples for airborne radar. The whole space-time plane is discretized into small grid points uniformly in presently available sparse recovery STAP methods, however, the clutter ridge is not located exactly on the pre-discretized grid points in non-sidelooking STAP radar. The dictionary mismatch effect degrades the performance of STAP significantly. In this paper, a gridless sparse recovery STAP method is proposed based on Atomic Norm Minimization (ANM-STAP), which utilizes the low-rank property of the clutter covariance matrix. In the proposed method, the clutter spectrum is precisely estimated in continuous space-time plane without dictionary mismatch. Numerical results show that the proposed method provides an improved performance to the sparse recovery STAP methods with discretized dictionaries.