Sparsity-aware space-time adaptive processing algorithms with L1-norm regularisation for airborne radar
Sparsity-aware space-time adaptive processing algorithms with L1-norm regularisation for airborne radar
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DOI：
10.1049/iet-spr.2011.0254
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发表时间：
2012-09
期刊：
影响因子：
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通讯作者：
Zhaocheng Yang;R. D. Lamare;Xiang Li
中科院分区：
文献类型：
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作者：
Zhaocheng Yang;R. D. Lamare;Xiang Li
本研究提出了适用于机载相控阵雷达应用的具有 L 1 范数正则化的新颖稀疏感知时空自适应处理 (SA-STAP) 算法。所提出的 SA-STAP 算法假设全秩 STAP 数据立方体的多个样本对于处理没有意义，并且最佳全秩 STAP 滤波器权重向量是稀疏或接近稀疏的。该方法的核心思想是将稀疏正则化（L 1 -范数类型）施加到最小方差STAP成本函数。在一些合理的假设下，作者首先提出了基于L 1 的样本矩阵求逆来计算最优滤波器权重向量。然而，它是不切实际的，因为它的矩阵求逆，在使用大型相控阵天线时需要很高的计算成本。为了以经济高效的方式计算 STAP 参数，作者设计了基于共轭梯度技术的低复杂度算法。与现有算法进行了计算复杂度比较并对所提出的算法进行了分析。模拟数据和山顶数据的仿真结果表明，所提出的算法实现了快速的信号干扰加噪声比收敛和良好的性能。
This study proposes novel sparsity-aware space-time adaptive processing (SA-STAP) algorithms with L 1 -norm regularisation for airborne phased-array radar applications. The proposed SA-STAP algorithms suppose that a number of samples of the full-rank STAP datacube are not meaningful for processing and the optimal full-rank STAP filter weight vector is sparse, or nearly sparse. The core idea of the proposed method is imposing a sparse regularisation ( L 1 -norm type) to the minimum variance STAP cost function. Under some reasonable assumptions, the authors firstly propose an L 1 -based sample matrix inversion to compute the optimal filter weight vector. However, it is impractical because of its matrix inversion, which requires a high computational cost when using a large phased-array antenna. In order to compute the STAP parameters in a cost-effective way, the authors devise low-complexity algorithms based on conjugate gradient techniques. A computational complexity comparison with the existing algorithms and an analysis of the proposed algorithms are conducted. Simulation results with both simulated and the Mountain-Top data demonstrate that fast signal-to-interference-plus-noise-ratio convergence and good performance of the proposed algorithms are achieved.