非均匀环境中的分布目标自适应检测

Adaptive Detection for Distributed Targets in Non-homogeneous Environments

该文研究了非均匀环境中的分布目标和多点目标的检测。其中,假设辅数据协方差矩阵服从以主数据协方差矩阵为条件的逆Wishart分布,且均值与之成比例。首先给出主数据协方差矩阵、比例因子和目标幅度的最大似然估计(MLE),然后基于贝叶斯理论和广义似然比(GLRT)判决准则提出了一种检测器。当目标只存在单个距离门时,检测器和自适应相干估计器(ACE)一致;当目标跨越多个距离门时,检测器和广义自适应子空间检测器(GASD)一致。但不同在于ACE和GASD都是基于未知的确定干扰协方差矩阵的。另外,该检测器具有恒虚警率(CFAR)特性,并且有很好的检测性能。

Adaptive detection for distributed target and multiple point targets in non-homogeneous environments is studied in this paper,where it is assumed that the covariance matrix of the secondary data follows inverse Wishart distribution conditioned on that of the primary data with its expectation proportional to it.The Maximum Likelihood Estimator（MLE） of the covariance matrix of the primary data,scale factor and target amplitude are firstly given and subsequently a detector is proposed based on Bayesian theory and Generalized Likelihood Ratio Test（GLRT） decision rule.The detector is coincident with the Adaptive Coherence Estimator（ACE） when the target exists in one range bin and it is consistent with the Generalized Adaptive Subspace Detector（GASD） when target extends more than one range bin.However,what makes it different is that the ACE and GASD are both based on unknown deterministic covariance matrix.Additionally,the detector has Constant False Alarm Rate（CFAR） and bears good performance.