Robust Detection in Compound Gaussian Clutter Based on Bayesian Framework
-
摘要: 复合高斯杂波中的纹理分量决定了杂波的非高斯性,而纹理分量的不确定性会影响常规检测器的性能。基于Bayes框架,该文采用先验分布描述杂波纹理分量的不确定性,分析先验模型选择对检测器检测性能与稳健性的影响。先验信息模型包括无信息先验分布和有信息先验分布。无信息先验分布包括Jeffery先验模型和广义无信息先验模型两种,所得到的检测器结构就是归一化匹配滤波器(NMF)。有信息先验模型采用共轭先验分布,得到的是一种知识辅助的归一化匹配滤波器(KA-NMF),该检测器结构与判决门限都是先验分布参数的函数,该文分析了KA-NMF检测性能对先验分布参数的敏感性。进一步采用无信息先验模型描述先验分布参数,可以获得分层Bayes归一化匹配滤波器(HB-NMF)。计算机仿真与实测海杂波数据分析结果表明,HB-NMF的性能与分布参数无关,稳健性优于KA-NMF,而检测性能优于NMF。Abstract: The texture component of compound Gaussian model determines the non-Gaussian characteristics of clutter, and the uncertainty of the texture component can result to the detection performance degradation of the conventional detectors. In this paper, based on the Bayesian framework, the prior distribution is used to denote the uncertainty of texture component, and the impact of the prior model on the robust detection performance is discussed. Two kinds of prior models are considered: non-informative prior model and the informative prior model. Non-informative prior models include the Jeffery prior model and generalized non-informative prior model, and the Normalized Matched Filter (NMF) is given using these prior models. Conjugate prior distribution is used as informative prior model, and Knowledge Aided NMF (KA-NMF) is given. The structure and threshold of KA-NMF are the function of the parameters of prior model. In this paper, the sensitivity of the detection performance of KA-NMF to the parameters of prior model is analyzed. Further more, the non-informative prior model is used to denote the parameters, and the Hierarchical Bayesian NMF (HB-NMF) is given. The computer simulation and real sea clutter data analysis results show that, the HB-NMF detection performance has no relation with the parameters of prior model, and its robustness and detection performance outperform the KA-NMF and NMF respectively.
计量
- 文章访问数: 2485
- HTML全文浏览量: 104
- PDF下载量: 899
- 被引次数: 0