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Knowledge-aided STAP with sparse-recovery by exploiting spatio-temporal sparsity

机译：通过利用时空稀疏性来实现知识恢复的STAP，具有稀疏恢复

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摘要

In this paper, novel knowledge-aided space-time adaptive processing (KA-STAP) algorithms using sparse representation/recovery (SR) techniques by exploiting the spatio-temporal sparsity are proposed to suppress the clutter for airborne pulsed Doppler radar. The proposed algorithms are not simple combinations of KA and SR techniques. Unlike the existing sparsity-based STAP algorithms, they reduce the dimension of the sparse signal by using prior knowledge resulting in a lower computational complexity. Different from the KA parametric covariance estimation (KAPE) scheme, they estimate the covariance matrix using SR techniques that avoids complex selections of the Doppler shift and the covariance matrix taper. The details of the selection of potential clutter array manifold vectors according to prior knowledge are discussed and compared with the KAPE scheme. Moreover, the implementation issues and the computational complexity analysis for the proposed algorithms are also considered. Simulation results show that our proposed algorithms obtain a better performance and a lower complexity compared with the sparsity-based STAP algorithms and outperform the KAPE scheme in presence of errors in prior knowledge.
机译：本文提出了一种利用时空稀疏性的稀疏表示/恢复（SR）技术，采用知识辅助的时空自适应处理（KA-STAP）算法，以抑制机载脉冲多普勒雷达的杂波。所提出的算法不是KA和SR技术的简单组合。与现有的基于稀疏性的STAP算法不同，它们通过使用先验知识来降低稀疏信号的维数，从而降低了计算复杂度。与KA参数协方差估计（KAPE）方案不同，他们使用SR技术估计协方差矩阵，从而避免了多普勒频移和协方差矩阵锥度的复杂选择。讨论了根据先验知识选择潜在杂波阵列流形矢量的细节，并将其与KAPE方案进行了比较。此外，还考虑了所提出算法的实现问题和计算复杂度分析。仿真结果表明，与基于稀疏的STAP算法相比，我们提出的算法具有更好的性能和更低的复杂度，并且在先验知识存在错误的情况下优于KAPE方案。

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