The sparse recovery space–time adaptive processing (SR-STAP) can reduce the requirements of clutter samples and suppress clutter effectively using limited training samples for airborne radar. Commonly, the whole angle-Doppler plane is uniformly discretized into small grid points in SR-STAP methods. However, the clutter patches deviate from the pre-discretized grid points in a non-sidelooking SR-STAP radar. The off-grid effect degrades the SR-STAP performance significantly. In this paper, a gridless SR-STAP method based on reweighted atomic norm minimization is proposed, in which the clutter spectrum is precisely estimated in the continuous angle-Doppler domain without resolution limit. Numerical simulations are conducted and the results show that the proposed method can achieve better performance than the SR-STAP methods with discretized dictionaries and the SR-STAP methods utilizing atomic norm minimization.

稀疏恢复时空自适应处理（SR-STAP）可以减少对杂波样本的要求，利用机载雷达有限的训练样本有效抑制杂波。通常，在 SR-STAP 方法中，整个角度多普勒平面被均匀地离散为小网格点。然而，杂波块偏离了非侧视 SR-STAP 雷达中预先离散化的网格点。离网效应显着降低了 SR-STAP 的性能。本文提出了一种基于重加权原子范数最小化的无网格SR-STAP方法，在无分辨率限制的连续角多普勒域中精确估计杂波谱。