复合高斯背景下基于最优控制参数的自适应检测器

Adaptive detector based on optimal control parameter in compound-Gaussian background

在复合高斯杂波背景下,针对检测器α-AMF利用采样协方差矩阵(SCM)估计方法不具备完全自适应性以及控制参数α不匹配的问题,首先,结合归一化采样协方差矩阵(NSCM)估计方法,提出α-AMF的SCM-NSCM组合估计方法;然后,拟合出检测器最优控制参数的经验公式,经验公式符合数值结果;最后,将α-AMF与改进的α-AMF的恒虚警率特性和检测性能进行对比分析.研究结果表明,在复合高斯环境下,基于SCM-NSCM估计的α-AMF受杂波尖峰的影响小于对比检测器,对杂波归一化协方差矩阵结构的变化具有很强的鲁棒性;在严重拖尾的非高斯环境中,所提出的自适应检测器性能明显优于对比检测器.

This paper addresses the problem that the detector α-AMF is not fully adaptive and the control parameter α is mismatched when using the sample covariance matrix（SCM） estimation in the compound-Gaussian background. Firstly,combined with the normalized sample covariance matrix（NSCM） estimation, a SCM-NSCM combination estimation method for the α-AMF is proposed. Then, the empirical formula of the optimal control parameter is simulated and it is consistent with the numerical results. Finally, the constant false alarm rate（CFAR） characteristics and the performance of the detectors are analyzed. The results show that the α-AMF based on SCM-NSCM estimation, which is less affected by the spikiness of clutter than the comparative detector, has strong robustness to the variation of the normalized covariance matrix of the clutters in the compound-Gaussian environment, and it exhibits better detection performance than the comparative detector in heavy-tailed clutter.