Signal Detection Based on Sigmoid Function in Non-Gaussian Noise
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摘要: 针对非高斯背景下的弱信号检测问题，该文提出一种基于Sigmoid函数的信号检测(SFD)方法。首先依据混合高斯模型对非高斯背景建模，在此基础上系统研究了参数k与SFD的检测性能以及检测特性的关系，确定了k的最佳的取值，并指出SFD在检测性能达到最优的同时也具有恒虚警特性。其次通过固定k值得到了一种新的非参量检测方法，较传统的匹配滤波性能有明显提升。最后进行仿真分析验证了SFD的有效性和优越性。Abstract: To solve the problem of weak signals detection in non-Gaussian background, a method based on Sigmoid function is proposed which is named Sigmoid Function Detector (SFD). Firstly, the non-Gaussian background is modeled as a mixed Gaussian model. Based on this, the relationship between parameter k and SFD's performance and characteristics are systematically analyzed. It is pointed out that SFD will be a constant false alarm detector when its detection performance is optimal. Secondly, a new non-parametric detector is proposed via fixing the parameter k, which has significant improvement over matched filter. Finally, simulation analysis is carried out to verify the effectiveness and superiority of SFD.
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图 2
${g_{{\rm{SF}}}}\left( x \right)$ 与${g_{{\rm{LO}}}}\left( x \right)$ ,${g_{{\rm{LM}}}}\left( x \right)$ 对比 -
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