Sensitivity Analysis of Knowledge Aided Detector in Non-Gaussian Clutter
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摘要: 先验信息的使用可以提高知识辅助检测器的探测性能,若先验信息与当前探测环境不匹配,检测器性能可能会受到影响。该文考虑一种复合高斯杂波下的知识辅助检测器,其采用逆伽马分布作为纹理分量先验分布,分析该检测器在不同杂波纹理分量模型参数条件下的检测性能。首先给出了先验模型参数失配条件下,虚警概率和Swerling I型目标检测概率的计算方法。然后在给定先验模型参数条件下,分析了杂波纹理分量分布参数对检测器性能的影响。理论分析表明,若杂波纹理分量分布参数位于某个区域以内时,检测器可以获得比模型匹配时更好的检测性能,计算机仿真验证了上述结论。Abstract: Prior information can be used to improve detection performance of 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 in compound Gaussian clutter is considered, for the inverse Gamma distribution is used as the prior distribution of clutter texture component, and the detection performance of this detector is analyzed with different clutter texture component model parameters. First, false alarm rate and detection probability of Swerling I target are given under the condition of mismatched prior information parameters. Second, the impact on the detection performance with clutter texture distribution parameters is analyzed under the conditions of given prior information parameters. Theoretical analysis results show that when the distribution parameters of clutter texture component are located in some area, the detection performance could be better than that with the prior information matchs the clutter environment. The computer simulation validates the conclusion.
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