Liu Kun, Wang Tong, Wu Jianxin, Chen Jinming
National Lab of Radar Signal Processing, Xidian University, Xi'an 710071, China.
School of Electronics and Communication Engineering, Sun Yat-sen University, Guangzhou 510275, China.
Sensors (Basel). 2021 Dec 23;22(1):77. doi: 10.3390/s22010077.
In the presence of unknown array errors, sparse recovery based space-time adaptive processing (SR-STAP) methods usually directly use the ideal spatial steering vectors without array errors to construct the space-time dictionary; thus, the steering vector mismatch between the dictionary and clutter data will cause a severe performance degradation of SR-STAP methods. To solve this problem, in this paper, we propose a two-stage SR-STAP method for suppressing nonhomogeneous clutter in the presence of arbitrary array errors. In the first stage, utilizing the spatial-temporal coupling property of the ground clutter, a set of spatial steering vectors with array errors are well estimated by fine Doppler localization. In the second stage, firstly, in order to solve the model mismatch problem caused by array errors, we directly use these spatial steering vectors obtained in the first stage to construct the space-time dictionary, and then, the constructed dictionary and multiple measurement vectors sparse Bayesian learning (MSBL) algorithm are combined for space-time adaptive processing (STAP). The proposed SR-STAP method can exhibit superior clutter suppression performance and target detection performance in the presence of arbitrary array errors. Simulation results validate the effectiveness of the proposed method.
在存在未知阵列误差的情况下，基于稀疏恢复的空时自适应处理（SR-STAP）方法通常直接使用无阵列误差的理想空间导向矢量来构建空时字典；因此，字典与杂波数据之间的导向矢量失配将导致SR-STAP方法的性能严重下降。为了解决这个问题，本文提出了一种两阶段SR-STAP方法，用于在存在任意阵列误差的情况下抑制非均匀杂波。在第一阶段，利用地面杂波的空时耦合特性，通过精细多普勒定位很好地估计了一组存在阵列误差的空间导向矢量。在第二阶段，首先，为了解决由阵列误差引起的模型失配问题，我们直接使用在第一阶段获得的这些空间导向矢量来构建空时字典，然后，将构建的字典与多测量矢量稀疏贝叶斯学习（MSBL）算法相结合进行空时自适应处理（STAP）。所提出的SR-STAP方法在存在任意阵列误差的情况下能够展现出卓越的杂波抑制性能和目标检测性能。仿真结果验证了所提方法的有效性。
