无参考数据的分布目标知识辅助检测

Knowledge-aided Detection of Distributed Targets without Secondary Data

该文利用待检测单元杂波协方差矩阵的先验信息,基于贝叶斯方法,研究无参考数据条件下的分布目标的知识辅助检测问题。首先针对非均匀场景,假定各个距离单元杂波协方差矩阵依概率1不相等,给出了广义似然比检验和最大后验-广义似然比检验两种检测器。然后针对均匀杂波场景,给出了单步和双步广义似然比检验两种检测器。进一步利用计算机仿真分析了先验模型失配条件下的检测器性能。分析结果表明,先验模型参数u较小时,检测器性能与先验模型匹配程度密切相关。当u趋于无穷大时,该文给出的几种检测算法性能趋于相同。

With prior information of clutter covariance matrix of cells under test, knowledge aided detection methods for distributed targets without secondary data are researched based on Bayesian approach. First, for the heterogeneous clutter environment that the clutter covariance matrix of each cell is not the same with probability one, the Generalized Likelihood Ratio Test （GLRT） and Maximum-A-Posterior （MAP） GLRT are proposed. Then, for the homogeneous clutter environment that the clutter covariance matrix of each cell is the same, the one step GLRT and two-step GLRT are proposed. Furthermore, the detection performance under the prior model mismatched condition is analyzed using computer simulation. The results show that, when the parameter u of prior information model is small, the detection performance of detectors is related to matching degree of prior information. And when the parameter u trends to infinity, all the detectors proposed in this paper have similar performance.