非高斯杂波下知识辅助检测的认知方法

发布时间：2018-04-09 18:35

本文选题：知识辅助检测器　切入点：复合高斯　出处：《电子学报》2014年06期

【摘要】：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.
[Abstract]:The use of prior information is one of the effective ways to improve the performance of radar target detection. However, the mismatch between prior information and current detection environment will seriously affect the performance of the detector.In this paper, considering the texture of inverse gamma distribution and the knowledge aided detection algorithm under complex Gao Si clutter, the formulas of false alarm rate (false alarm rate) and detection probability of Swerling I target under the condition of prior model mismatch (inverse gamma distribution parameter mismatch) are derived.The quantization relationship between detection performance and model parameter mismatch is obtained.Two sets of knowledge aided detectors with different parameters are used to detect the current clutter environment. The estimation of the parameters of the current clutter environment model is realized by evaluating the performance of the detector.The results of computer simulation and the analysis of measured data show that the knowledge aided detector with cognitive method can obtain better detection performance than the conventional detector.
【作者单位】：西安电子科技大学雷达信号处理国家重点实验室;空军工程大学信息与导航学院;国家知识产权局专利局专利审查协作中心;
【基金】：中国博士后科学基金(No.2012M521744) 国家自然科学基金(No.61271292) 陕西省自然科学基金(No.2011JQ8040)
【分类号】：TN957.51

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