最大平均相关高度(MACH:Maximum Average Correlation Height)滤波器是一种重要的基于相关的模式识别方法.滤波器由训练数据线性构造而成,具有良好的畸变容忍能力,在线性高斯噪声条件下具有理论最优性.为将算法适用于广泛的非线性、非高斯情形,本文引入一种新的度量函数相关熵,可隐性地将输入数据通过非线性变换映射到特征空间；并在新的空间中提出了基于相关熵的MACH滤波器构造方法.最后将此方法应用于合成孔径雷达(SAR:Synthetic Aperture Radar)图像目标分类进行了实验,在接收机工作性能曲线和峰值旁瓣比的比对中,本文算法的性能均有所提升.%Maximum average correlation height ( MACH) filter is formulated by linearly combining the training data, which is statistically optimum and fairly robust for finding targets in clutter when the Gaussian assumption holds. This research proposes a nonlinear extension to the MACH filter by correntropy function which can induce a new feature space. Thus it is possible to construct linear filter equations in the new space, and the proposed filter has an improved performance due to the nonlinear relation between the feature space and input space. The algorithm is applied to synthetic aperture radar image recognition and exhibits better performance under peak-sidelobe-ratio and receiver-operating-characteristic criteria.
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