An Overview on Sparse Recovery-based STAP
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摘要: 该文介绍了基于稀疏恢复(Sparse Recovery, SR)的空时2 维自适应处理技术(Space-Time Adaptive Processing, STAP)的研究背景、意义和具体实现方法。首先探讨了将稀疏恢复引入STAP 领域的意义和价值,揭示了在杂波非均匀环境下引入稀疏恢复的潜在优势,分析了稀疏恢复STAP 技术的数学意义。并在此基础上,系统梳理和总结了该研究方向的研究现状和已有成果,介绍了均匀线性阵列条件下稀疏恢复STAP 技术的基本框架、多观测向量问题、格点对不准问题、直接数据域稀疏恢复STAP、共型阵条件下基于稀疏恢复的STAP 方法等具体研究内容。最后,总结了基于稀疏恢复STAP 技术的框架和结构,并以此为基础对后续研究工作的方向和前景进行了探讨。
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关键词:
- 机载雷达 /
- 杂波抑制 /
- 稀疏恢复(SR) /
- 空时自适应处理(STAP)
Abstract: This paper gives a brief review on the Sparse-Recovery (SR)-based Space-Time Adaptive Processing (STAP) technique. First, the motivation for introducing sparse recovery into STAP is presented. Next, the potential advantages and mathematical explanation of the sparse-recovery-based STAP are discussed. A major part of this paper presents the state-of-art research results in spatio-temporal spectrum-sparsity-based STAP, including the basic frame, off-grid problem, multiple measurement vector problem, and direct domain problem. The sparse-recovery-based STAP on conformal array problem is also introduced. Finally, a summary of sparse-recovery-based STAP is provided, and the problems that need to be solved and some potential research areas are discussed.
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