论文标题
使用重新持续的原子规范最小化，无网固定钉子的无网稀疏稀疏恢复
Gridless Super-Resolution Sparse Recovery for Non-sidelooking STAP using Reweighted Atomic Norm Minimization
论文作者
论文摘要
稀疏的恢复时空自适应处理（Stap）可以减少杂物样品的要求，并使用有限的训练样品有效地抑制混乱。在目前可用的稀疏恢复订书机方法中，整个角度多普勒平面被统一地分为小网格点，但是，杂物脊并未完全位于非固定拼写式缝隙雷达中的预涂性网格点上。离网效应显着降低了Stap的性能。在本文中，提出了基于重新加权的原子规范最小化提出的无网稀疏恢复方法，其中在没有分辨率限制的情况下，精确地在连续的角度多普勒平面中精确估算了杂波谱。数值结果表明，提出的方法利用原子规范最小化的词典为稀疏的恢复订阅方法提供了改进的性能。
Sparse recovery Space-time Adaptive Processing (STAP) can reduce the requirements of clutter samples, and suppress clutter effectively using limited training samples for airborne radar. The whole angle-Doppler 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 off-grid effect degrades the performance of STAP significantly. In this paper, a gridless sparse recovery STAP method is proposed based on reweighted atomic norm minimization, in which the clutter spectrum is precisely estimated in continuous angle-Doppler plane without resolution limit. Numerical results show that the proposed method provides an improved performance to the sparse recovery STAP methods with discretized dictionaries and STAP method utilizing atomic norm minimization.
