Study of Gaussianization Processing Based on Symmetric Alpha-stable Distribution Modeling
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摘要: 针对非高斯背景干扰下的弱信号检测需求，在综述高斯化处理与扩展匹配滤波概念及思路的基础上，该文提出了两种基于对称α稳定分布建模的高斯化处理方法，构建了相应的扩展匹配滤波检测器，并对比早先建立的混合高斯分布下的高斯化处理，对其高斯化效果、对应检测性能、运行速度等进行了仿真研究。研究表明，高斯化处理可以降低背景干扰的非高斯性，从而提高后续匹配滤波的检测性能；基于对称α稳定分布的高斯化处理，在保持性能的同时，具有更高的运行效率。Abstract: Considering the requirement for weak signal detection in non-Gaussian background interference, after conceptions and ideas summarizing for Gaussianization processing and extended matched filter, two Gaussianizaiton filters and corresponding detections are proposed based on symmetric alpha-stable distribution modeling, comparing with those under Gaussian mixture modeling proposed earlier. All these Gaussianization filters and extended matched filters are realized in simulation. Their performances, such as Gaussianzing effect, detecting capability and running time are studies in system. Some conclusions are reached: Gaussianization can improve the detecting performance of the succeeding matched filter because of its restraining of big impulsive samples; Gaussianization filters under symmetric alpha-stable distribution modeling have higher operatiing efficiency while their peformance are similar to those under Gaussian mixture modeling.
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表 1 各EMF运行时间统计比较(基于1000次检测)
CMF EMF-Ua EMF-Ugc EMF-Ugm 均值 (ms) 0.3746 1.4699 0.4112 60.0990 方差 (ms2) 0.0014 0.0039 0.0012 43.6956 
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