An Overview on Sparse Recovery Space-Time Adaptive Processing Technique

doi:10.3969/j.issn.0372-2112.2019.03.033

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An Overview on Sparse Recovery Space-Time Adaptive Processing Technique

更新时间：2025-07-02

- An Overview on Sparse Recovery Space-Time Adaptive Processing Technique
- Acta Electronica Sinica Vol. 47, Issue 3, Pages: 748-756(2019)
- 作者机构：
  
  1. 空军预警学院,湖北,武汉,430019
  2. 海军工程大学电子工程学院,湖北,武汉,430033
  3. 中山大学电子与通信工程学院,广东,广州,510006
  4. 空军预警学院,湖北,武汉,430019
  5. 海军工程大学电子工程学院,湖北,武汉,430033
  6. 中山大学电子与通信工程学院,广东,广州,510006
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61871397, No.61501506)
- DOI：10.3969/j.issn.0372-2112.2019.03.033
  CLC： TN957
- Published Online：25 March 2019，
  
  Published：2019
- 稿件说明：
移动端阅览
An Overview on Sparse Recovery Space-Time Adaptive Processing Technique[J]. Acta Electronica Sinica, 2019, 47(3): 748-756.
DOI：

An Overview on Sparse Recovery Space-Time Adaptive Processing Technique[J]. Acta Electronica Sinica, 2019, 47(3): 748-756. DOI： 10.3969/j.issn.0372-2112.2019.03.033.

摘要

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.

Abstract

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.

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