稀疏恢复空时自适应处理技术研究综述

相较于传统空时自适应处理（STAP）技术，稀疏恢复（SR）STAP技术在小样本条件下杂波抑制性能显著提升，因此适用于现实非均匀杂波环境.本文首先阐述了SR STAP基本原理，分析了机载雷达杂波空时稀疏特性；然后总结了SR STAP发展历史与现状，并在此基础上针对其相关科学问题进行了探讨，包括：空时谱估计还是杂波抑制、单观测样本还是多观测样本、白化还是置零、重构算法参数依赖还是不依赖、非平稳杂波下是否适用及干扰条件下是否可行；最后给出了当前SR STAP技术走向实用化过程中所面临的关键问题，即网格失配和空域误差影响，并分别讨论了无网格压缩感知和字典自校正的解决途径.

An Overview on Sparse Recovery Space-Time Adaptive Processing Technique

Compared with conventional space-time adaptive processing (STAP) technique,sparse recovery (SR) STAP technique can significantly improve the clutter suppression performance in the case of limited training samples,and hence is well suited for practical non-homogeneous clutter environment.Firstly,the paper describes the principle of SR STAP,and analyzes the clutter sparsity in space-time plane for airborne radar.Then the development and current status of SR STAP is summarized.On this basis,some key issues about the technique are discussed which include space-time spectrum estimation or clutter suppression,single or multiple measurements,clutter whitening or nulling,parameter dependence or independence for recovery algorithms,whether applicable for non-stationary clutter environment,and whether feasible under the condition of jamming.Finally,key problems confronted in the real-world applications for sparse recovery STAP technique are presented,which include off-grid effect,influence of spatial errors,and huge computational cost.Meanwhile,effective ways including gridless compressive sensing and self-calibration of overcomplete dictionary are respectively discussed to solve above problems.