Sparse Representation Based Algorithm for Airborne Radar in Beam-Space Post-Doppler Reduced-Dimension Space-Time Adaptive Processing
Sparse Representation Based Algorithm for Airborne Radar in Beam-Space Post-Doppler Reduced-Dimension Space-Time Adaptive Processing
复制标题
基于稀疏表示的机载雷达波束空间后多普勒降维空时自适应处理算法
DOI：
10.1109/access.2017.2689325
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发表时间：
2017-01-01
期刊：
影响因子：
3.9
通讯作者：
Feng, Weike
中科院分区：
文献类型：
--
作者：
Guo, Yiduo;Liao, Guisheng;Feng, Weike
提出一种基于稀疏表示的高效、减少训练样本的机载雷达地杂波抑制空时自适应处理（STAP）算法。首先，回顾了基于样本矩阵求逆的STAP和基于稀疏表示（SR）的STAP算法的原理和问题。然后，通过利用局部光束和多普勒域中杂波的固有稀疏性质，考虑杂波的局部时空谱（LSTS）的概念。为了以经济高效的方式使用稀疏表示技术估计 LSTS，设计了可变时空掩模矩阵。最后，基于估计的LSTS计算降维杂波加噪声协方差杂波矩阵和相应的自适应权重向量。模拟数据和山顶数据的数值结果表明，与现有的基于 SR 的 STAP 算法相比，新算法只需一个训练距离单元即可提供出色的杂波抑制和运动目标检测性能，并且可显着节省计算量。
An efficient and training-sample-reducing space-time adaptive processing (STAP) algorithm based on sparse representation for ground clutter suppression in airborne radar is proposed in this paper. First of all, the principle and problems of sample matrix inversion-based STAP and sparse representation (SR)-based STAP algorithms are reviewed. Then, the conception of the local space-time spectrum (LSTS) of clutter is considered by exploiting the intrinsic sparsity nature of clutter in local beams and the Doppler domain. To estimate the LSTS using the sparse representation technique in a cost-effective way, a variable space-time mask matrix is designed. Finally, the reduced-dimension clutter plus noise covariance clutter matrix and the corresponding adaptive weight vector are calculated based on the estimated LSTS. Numerical results with both simulated data and Mountain-Top data demonstrate that the new algorithm provides an excellent performance of clutter suppression and moving target detection with only one training range cell and significant computational savings compared with existing SR-based STAP algorithms.