$L_1$ -Regularized STAP Algorithms With a Generalized Sidelobe Canceler Architecture for Airborne Radar
$L_1$ -Regularized STAP Algorithms With a Generalized Sidelobe Canceler Architecture for Airborne Radar
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DOI：
10.1109/tsp.2011.2172435
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发表时间：
2012-02
影响因子：
5.4
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
在本文中，我们提出了适用于机载雷达应用的新型l1正则化空时自适应处理（STAP）算法，该算法具有广义旁瓣消除器架构。所提出的方法假设在阻塞过程的输出处的一些样本不需要用于旁瓣消除，这导致STAP滤波器权重向量的稀疏性。其核心思想是对最小方差准则施加稀疏正则化（l1范数类型）。通过求解该优化问题，提出了一种基于l1正则化的递归最小二乘（l1-based RLS）自适应算法。我们还讨论了该算法的SINR稳态性能和惩罚参数的设置。为了自适应地设置惩罚参数，针对基于l1的RLS算法提出了两种切换方案。计算复杂度分析表明，该算法与传统的RLS算法具有相同的复杂度（O（（NM）2）），其中NM是滤波器权重向量长度），但明显低于加载样本协方差矩阵求逆算法（O（（NM）3））和压缩感知STAP算法（O（（NsNd）3），其中N8 Nd>; NM是角度-多普勒平面尺寸）。仿真结果表明，所提出的STAP算法收敛速度快，并提供了一个SINR的改善，使用少量的快照。
In this paper, we propose novel l1-regularized space-time adaptive processing (STAP) algorithms with a generalized sidelobe canceler architecture for airborne radar applications. The proposed methods suppose that a number of samples at the output of the blocking process are not needed for sidelobe canceling, which leads to the sparsity of the STAP filter weight vector. The core idea is to impose a sparse regularization (l1-norm type) to the minimum variance criterion. By solving this optimization problem, an l1-regularized recursive least squares (l1-based RLS) adaptive algorithm is developed. We also discuss the SINR steady-state performance and the penalty parameter setting of the proposed algorithm. To adaptively set the penalty parameter, two switched schemes are proposed for l1-based RLS algorithms. The computational complexity analysis shows that the proposed algorithms have the same complexity level as the conventional RLS algorithm (O((NM)2)), where NM is the filter weight vector length), but a significantly lower complexity level than the loaded sample covariance matrix inversion algorithm (O((NM)3)) and the compressive sensing STAP algorithm (O((NsNd)3), where N8Nd >; NM is the angle-Doppler plane size). The simulation results show that the proposed STAP algorithms converge rapidly and provide a SINR improvement using a small number of snapshots.