一种基于联合稀疏恢复的空时自适应处理方法

A Space-time Adaptive Processing Algorithm Based on Joint Sparse Recovery

基于杂波谱稀疏恢复的空时自适应处理(STAP)技术可显著降低对训练样本的需求,因此适用于非均匀杂波环境。然而,现有杂波谱稀疏恢复STAP方法均是基于单样本恢复或多样本分别独立恢复后联合处理,并没有同时利用多个样本中的信息,而且恢复性能易受噪声影响。针对上述问题,该文提出一种基于杂波子空间的联合稀疏恢复STAP方法。该方法可充分利用多个训练样本中的杂波信息对杂波谱进行恢复,并在噪声环境下具有稳健的杂波抑制性能。仿真实验结果验证了所提方法的有效性。

Sparse recovery Space-Time Adaptive Processing （STAP） methods for obtaining the clutter spectrum require few training samples and can effectively suppress clutter in nonhomogeneous clutter environments. However, presently available sparse recovery STAP methods only use single training samples to recover the clutter spectrum, neglecting information from multiple samples. Moreover, the recovery performance of the abovementioned methods is sensitive to noise. In this study, a subspace-based jointly sparse recovery method is proposed. The information from multiple training samples is fully used and robust clutter suppression performance in noisy environments is achieved. Simulation results show the effectiveness of the proposed method.