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本论文基于 SDFs滤波器设计方法 ,采用神经网络中的 Monte Carlo优化学习算法 ,对二值化纯相位滤波器进行了优化设计。优化后的匹配空间滤波器使相关峰值和信噪均有较大提高 ,同时输出结果有了很大程度改善。
Based on the SDFs filter design method, this thesis uses Monte Carlo optimization learning algorithm in neural network to optimize the binarization pure phase filter. The optimized matched spatial filter increases the correlation peaks and signal-to-noise, while the output results have been greatly improved.