基于MFCC与PNN的高精密航天器多余物材质特征识别方法

A method for feature identification of remainder material in high precision spacecraft based on MFCC and PNN

针对不同材质的高精密航天器多余物信号检测存在特征重叠、可重复性较差的问题,提出基于梅尔频率倒谱系数(MFCC)与概率神经网络(PNN)的多余物材质特征识别方法。借鉴语音识别技术,设计了一种基于能量加权MFCC的多余物材质脉冲特征提取方法;构建了基于MFCC和优化PNN的单个多余物材质脉冲分类模型;利用每个多余物材质脉冲的分类信息构建多余物材质可信度,实现对铝屑、焊锡、塑料和橡胶4种典型材质的识别。经实验验证,该分类模型对单个多余物材质的识别准确率均在90%以上,对2个多余物材质的识别准确率均在80%以上。

Aiming at the problems of feature overlap and poor repeatability in the signal detection for different remainder materials of high precision spacecraft,a method for feature identification of remainder materials based on Mel frequency cepstrum coefficient(MFCC)and probabilistic neural network(PNN)was proposed.Inspired by the speech recognition technology,a method for pulse feature extraction of remainder material based on energy weighted MFCC was designed.A model for pulse classification of individual remainder material based on MFCC and optimized PNN was constructed.The pulse classification information of each remainder material was used to build the credibility of the remainder material,and the identification of four kinds of typical materials,including aluminum,solder,plastic and rubber,was realized.The experimental results show that the identification accuracy is above 90%for single remainder material,and is above 80%for two kinds of remainder materials.