非高斯杂波下自适应雷达目标检测新方法

A Novel Adaptive Radar Target Detector for Non-Gaussian Clutter

在球不变随机向量(SIRV)非高斯杂波背景下,研究了多脉冲相参雷达目标的自适应检测问题。假设杂波具有相同的协方差矩阵结构和可能相关的纹理分量,提出了新的协方差矩阵估计器,并获得了相应的自适应归一化匹配滤波器(ANMF)。理论分析表明,在估计杂波分组大小与实际情况匹配时,所获得的ANMF对杂波功率水平和协方差矩阵结构均具有恒虚警率(CFAR)特性。仿真结果表明:当估计的杂波分组大小失配时,所获得的ANMF具有近似CFAR特性,并进一步分析了不同参数变化对所提检测器性能的影响。与已有的ANMF相比,所获得的ANMF具有更好的检测性能,且迭代次数更小,其相对于已知杂波协方差矩阵的最优归一化匹配滤波器(NMF)的检测损失也更小,具有很好的实际应用前景。

In the clutter modeled as spherically invariant random vector（SIRV）,the adaptive coherent radar target detection is addressed.The SIRVs are assumed to possess the same covariance matrix and possibly correlated texture components.A novel covariance matrix estimator is proposed,and then its corresponding adaptive normalized matched filter（ANMF）is obtained.Theoretical analysis shows that,when there exists a match between the estimated clutter group size and the actual one,the proposed ANMF guarantees a constant false alarm rate（CFAR）property to the clutter covariance matrix and the clutter power levels.Moreover,the performance assessments conducted by Monte Carlo simulation show that the proposed ANMF is almost CFAR for mismatch cases.Compared with the existing ANMF,the proposed ANMF performs better with less computational burden.Furthermore,it has an acceptable loss with respect to its nonadaptive normalized matched filter（NMF）.