非均匀杂波环境下基于贝叶斯方法的自适应检测

Adaptive Detection Based on Bayesian Approach in Heterogeneous Environments

对于非均匀杂波环境下信号自适应检测问题,由于待测数据样本的协方差矩阵与训练数据的协方差矩阵不相同,造成检测性能下降,针对此问题本文提出了基于贝叶斯方法的广义似然比检测器(Bayesian generalized likelihood ratio test,B-GLRT).通过对非均匀杂波环境下协方差矩阵间的关系进行统计建模,使在B-GLRT的设计过程中能够结合杂波的非均匀性,并且这种非均匀性在统计模型中可以通过标量参数调节.同时通过对协方差矩阵选择合适的先验分布,使B-GLRT能够融合有助于提高检测性能的先验知识.通过仿真实验,验证了B-GLRT的检测性能高于传统的非贝叶斯检测器,并且分析了杂波环境非均匀性和先验信息对自适应检测性能的影响.

The performance of adaptive detection of an interested signal degrades when the environment is heterogeneous,i.e.,the training data samples used for adaption do not share the same covariance matrix as the cell under test（CUT）.To circumvent the problem,a Bayesian generalized likelihood ratio test（B-GLRT） detector is derived.On the one hand,the heterogeneity is considered at the design stage of B-GLRT by means of the statistical modeling of the covariance matrixes of CUT and training data in heterogeneous environment.Meanwhile,the degree of heterogeneity can be tuned through scalar.On the other hand,a prior distribution is assigned to covariance matrix to exploit some prior knowledge for performance improvement.Numerical simulations show that B-GLRT outperforms the conversional non-Bayesian detectors.Meanwhile,the influence of heterogeneity and prior knowledge on detection performance is illustrated.