Ren Bing, Wang Tong
National Laboratory of Radar Signal Processing, Xidian University, Xi'an 710071, China.
Sensors (Basel). 2022 Sep 13;22(18):6917. doi: 10.3390/s22186917.
Space-time adaptive processing (STAP) is a well-known technique for slow-moving target detection in the clutter spreading environment. For an airborne conformal array radar, conventional STAP methods are unable to provide good performance in suppressing clutter because of the geometry-induced range-dependent clutter, non-uniform spatial steering vector, and polarization sensitivity. In this paper, a knowledge aided STAP method based on sparse learning via iterative minimization (SLIM) combined with Laplace distribution is proposed to improve the STAP performance for a conformal array. The proposed method can avoid selecting the user parameter. the proposed method constructs a dictionary matrix that is composed of the space-time steering vector by using the prior knowledge of the range cell under test (CUT) distributed in clutter ridge. Then, the estimated sparse parameters and noise power can be used to calculate a relatively accurate clutter plus noise covariance matrix (CNCM). This method could achieve superior performance of clutter suppression for a conformal array. Simulation results demonstrate the effectiveness of this method.
空时自适应处理（STAP）是一种在杂波扩展环境中用于检测慢速移动目标的著名技术。对于机载共形阵列雷达，由于几何形状引起的距离相关杂波、非均匀空间导向矢量和极化敏感性，传统的STAP方法在抑制杂波方面无法提供良好的性能。本文提出了一种基于迭代最小化稀疏学习（SLIM）并结合拉普拉斯分布的知识辅助STAP方法，以提高共形阵列的STAP性能。该方法可以避免选择用户参数。该方法利用分布在杂波脊上的待测距离单元（CUT）的先验知识，构造了一个由空时导向矢量组成的字典矩阵。然后，可以使用估计的稀疏参数和噪声功率来计算相对准确的杂波加噪声协方差矩阵（CNCM）。该方法可以实现共形阵列杂波抑制的优越性能。仿真结果证明了该方法的有效性。
