海杂波背景下雷达目标贝叶斯检测算法

Knowledge-based adaptive detection of radar targets in sea clutter background

针对非高斯海杂波背景下参考单元数目较少时自适应检测器性能损失严重的问题,提出了两种基于协方差矩阵先验分布信息的自适应检测算法,将海杂波建模为复合高斯模型,纹理分量建模为一个服从广义逆高斯分布的随机变量。首先将散斑的协方差矩阵建模为一个服从复逆威沙特分布的随机矩阵,然后根据广义似然比准则设计了一种不依赖于参考单元数据的自适应检测器。由于在参考单元数目增多时,不依赖于辅助数据的检测器性能会差于传统的自适应检测器,所以根据最大后验检验准则,并使用参考单元数据又设计了一种依赖于参考单元数据和先验知识的自适应检测器。实验结果表明,在参考单元数目较少时,所提出的两种检测器具有较好的检测性能;在不同的参考单元数目下,所提出的依赖于参考单元数据和先验知识的自适应检测器都具有最优的检测性能。

This paper focuses on the problem of radar targets detection in the compound-Gaussian sea clutter on the condition with the limited secondary data.The texture is modeled by the generalized inverse Gaussian distribution.Two adaptive detectors based on a priori knowledge of the speckle covariance matrix are proposed.First,the inverse complex Wishart distribution is exploited to model the speckle covariance matrix,and then an adaptive detector without using the secondary data is designed according to the generalized likelihood ratio.According to the maximum posterior test criterion,the secondary data are used to design an adaptive detector with secondary data and prior knowledge.Experimental results show that when the number of secondary cells is small,the two detectors proposed in this paper have a better detection performance than the GLRT-GIG detector.With different numbers of secondary cells,the proposed adaptive detector depending on the secondary data and a prior knowledge has the best performance.