非高斯杂波下知识辅助检测的认知方法

先验信息的使用是提高雷达目标检测性能的有效途径之一，然而先验信息与当前探测环境的失配会严重影响到检测器的性能.本文考虑逆伽马分布纹理、复合高斯杂波下的知识辅助检测算法，推导了先验模型失配条件下(逆伽马分布参数失配)检测器的虚警率和Swerling I型目标的检测概率计算公式，获得了检测性能与模型参数失配之间的量化关系.利用两组不同参数的知识辅助检测器对当前杂波环境进行探测，通过评估检测器的性能，实现了当前杂波环境模型参数的估计.计算机仿真和实测数据的分析结果表明，采用认知方法的知识辅助检测器较常规检测器而言，能够获得更好的检测性能.

Cognitive Method for Knowledge Aided Detection in Non-Gaussian Clutter

Prior information can be used to improve detection performance for knowledge aided detectors,but the detection performance may be affected by the mismatches between the prior information and current clutter environment.In this paper,the knowledge aided detector for compound Gaussian clutter with inverse gamma distribution texture is considered,and the formula of false alarm rate and probability of detection for Swerling I target of the detector are derived with prior model mismatches (mismatched parameters of inverse gamma distribution).The quantitative relationship between detection performance and mismatched prior model is obtained.Using two detectors with different parameters for the current clutter environment and assessing the detection performance,the parameters of current clutter environment can be estimated.The results of computer simulation and live data analysis show that,the knowledge aided detector with cognitive method can achieve better detection performance than conventional detectors.