Fu Dongning, Liao Guisheng, Xu Jingwei
National Lab of Radar Signal Processing, School Electronic Engineering, Xidian University, Xi'an 710071, China.
Sensors (Basel). 2021 Apr 29;21(9):3108. doi: 10.3390/s21093108.
For statistic space-time adaptive processing (STAP), a critical issue is estimating the clutter covariance matrix (CCM). However, sufficient training samples are difficult to obtain that satisfy the independent and identically distributed (IID) condition. It is because of the realistic heterogeneous environment faced by airborne radar. Moreover, one should eliminate contaminated training samples before CCM estimation. Aiming at the problems of the computational complexity and susceptibility to the outlier of the traditional generalized inner product (GIP) method, a clutter subspace-based training sampling selecting method is proposed combined with specific distribution in the space-time plane of clutter spectrum. Theoretical analysis and simulation results verified the proposed method and indicate that the proposed method is easy to construct CCM and has lower computational complexity and sensitivity to outliers.
对于统计时空自适应处理（STAP）而言，一个关键问题是估计杂波协方差矩阵（CCM）。然而，难以获得满足独立同分布（IID）条件的足够训练样本。这是由于机载雷达面临的实际非均匀环境所致。此外，在CCM估计之前应消除受污染的训练样本。针对传统广义内积（GIP）方法计算复杂度高且易受异常值影响的问题，结合杂波谱时空平面中的特定分布，提出了一种基于杂波子空间的训练样本选择方法。理论分析和仿真结果验证了该方法，表明该方法易于构建CCM，且具有较低的计算复杂度和对异常值的敏感度。
