A Novel Method for Nonlinear Processing in Impulsive Noise Based on Gaussianization and Generalized Matching
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摘要: 针对脉冲型噪声，该文提出一种新的非线性处理方法，即高斯化-广义匹配(GGM)处理。GGM方法基于高斯化处理与广义匹配滤波，可结合非参数的概率密度估计进行设计，解决噪声模型未知时的非线性处理问题。该文以脉冲型噪声
${\rm S\alpha S}$ 分布模型为例，分析GGM方法的特点和性能；再结合Class A噪声模型，讨论GGM设计作为非参数方法相比模型假设失配的优势；引入效能函数，验证GGM方法在恒虚警技术中的运用。结果表明，在已知噪声分布情况下，GGM方法具有次优检测性能；当噪声模型未知时，非参数GGM设计能保持稳健性能，优于模型失配下的处理。并且，GGM设计对样本数目要求不高，为噪声特性不明或时变的场景提供了一种新的信号处理方法。Abstract: A method based on Gaussianization and generalized matching, called Gaussianization-Generalized Matching (GGM) method is proposed, for nonlinear processing in impulsive noise. The GGM method can be designed based on noise samples, aided by nonparametric probability density estimation. Thus the GGM design is suitable for nonlinear processing in unknown noise models. The GGM method in the${\rm S\alpha S}$ model is analyzed, and also the comparison with another approach is presented based on unmatched noise model assumption in the Class A noise. The GGM method is applied to the constant false alarm rate technique via the efficacy function. Simulation and analysis results show that the GGM design is sub-optimal, works robustly when the noise model is unknown, and raises a low requirement on the sample number. Thus, the GGM method provides a promising choice when the noise model is unclear or time-varying.-
Key words:
- Impulsive noise /
- Nonlinear processing /
- Gaussianization /
- Generalized matching /
- Efficiency function
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