利用先验信息可以提高雷达目标的探测能力,若先验信息与当前探测环境不匹配,知识辅助检测器的性能会受到影响.本文针对Bayes框架下的复合高斯杂波中的知识辅助检测器,提出了杂波纹理分量先验分布参数的感知方法.首先阐述了知识辅助检测器先验信息感知的一般方法.然后针对基于杂波纹理分量先验信息的知识辅助检测器结构,建立了先验模型参数失配与知识辅助检测器检测性能之间的量化关系.进一步利用知识辅助检测器对当前杂波场景进行探测,获得检验统计量和虚警率测量值,从而构造纹理分量分布参数的约束关系.通过分析多个约束关系的交点,获得杂波纹理分量先验分布参数的感知值.计算机仿真分析了这种感知方法的可行性,并利用实测杂波数据对感知方法进行了验证.通过知识辅助检测器检测性能对比分析,采用感知方法获得的先验信息模型参数能够进一步提高检测器的性能. The priori information can improve the detection ability of the radar target.If the prior information does not match with the current detection environment, the performance of the knowledge-based detector will be affected.In this paper, aiming at the knowledge-aided detector in the complex Gaussian clutter under the Bayes framework, A method to detect the priori distribution of clutter texture components is proposed.Firstly, the general method of knowledge-based detection of knowledge-based detectors is described.And then, aiming at the structure of knowledge-aided detector based on the prior information of clutter texture components, Model parameters mismatch and knowledge-based detector detection performance of the quantitative relationship between the further use of knowledge-based detector to detect the current clutter scene to obtain test statistics and false alarm rate measurements to construct the texture component distribution constraints The relationship between them is obtained by analyzing the intersection points of multiple constraints.The computer simulation analyzes the feasibility of this sensing method and verifies the sensing method by using the clutter data obtained by the method of Comparative analysis of the detection performance of knowledge-based detectors, a priori information model parameters Possible to further improve the performance of the detector.