基于KNN分类算法的n-γ脉冲信号甄别仿真研究

Simulation of n-γpulse signal discrimination based on KNN classification algorithm

利用脉冲形状甄别(PSD)法区分中子和γ射线脉冲信号是核探测过程中一项重要的任务。本文基于Labview平台实现了n/γ脉冲信号的仿真及信号预处理过程,分别利用传统的甄别方法电荷比较法、脉冲梯度分析(PGA)法及上升时间法对所产生的n/γ脉冲信号进行甄别,筛选出以上3种甄别方法结果一致的中子和γ射线混合脉冲信号作为KNN分类算法的训练集。通过训练样本构建KNN分类模型,使得能够通过该模型实现中子和γ射线脉冲信号的分类。结果表明,基于KNN分类算法的中子和γ射线脉冲信号甄别准确率高达99.58%,与电荷比较法,上升时间法和PGA方法相比,甄别错误率显著降低。并且KNN分类算法原理简单,易于实现,因此可应用于实际混合场中的n-γ脉冲甄别。

Using pulse shape discrimination(PSD)to distinguish between neutrons and gamma rays is an important task in the process of nuclear detection.Based on the Labview platform,this paper realizes the simulation and signal preprocessing process of n/γpulse signal.The traditional discrimination method,charge comparison method,pulse gradient analysis(PGA)method,and rise time method are used to perform the n/γpulse signal screening,screening out the neutron andγ-ray mixed pulse signals with the same results of the above three screening methods as the training set of the KNN classification algorithm.The KNN classification model is constructed by training samples,so that the classification of neutron and gamma-ray pulse signals can be realized through this model.The results show that the accuracy of neutron and gamma-ray pulse waveform discrimination based on the KNN classification algorithm is as high as 99.58%.Compared with the charge comparison method,the rise time method and the PGA method,the discrimination error rate is significantly reduced.And the KNN classification algorithm is simple in principle and easy to implement,so it can be applied to the discrimination of n-γpulses in the actual mixed field.