华中科技大学学报：自然科学版2012，Vol.40Issue(7)：24-28,5.
主成分分析在震动信号目标识别算法中的应用
Application of principal component analysis in target recognition algorithm of seismic signals
摘要
Abstract
In order to improve the algorithm of ground moving targets based on seismic signals,an algorithm of second feature extraction based on principal component analysis（PCA）was presented.First the target characteristics of seismic signals caused by ground moving targets were analyzed and multi-dimensional feature vectors were extracted.Then the large number of feature vectors was analyzed through principal component analysis.After the correlation between the feature vector was removed,the integrated feature vector was extracted and used in classifier to obtain result of target recognition.Based on real seismic signals of ground targets,experiment results indicate that this method can effectively decrease the dimension and correlation of feature vectors,reduce the difficulty and classifier training time,and improve the performance of classification,providing an idea for target recognition of seismic signals.关键词
目标识别/识别算法/主成分分析/震动信号/特征提取Key words
target recognition/recognition algorithm/principal component analysis/seismic signals/feature extraction分类
计算机与自动化引用本文复制引用
鲍必赛,楼晓俊,李隽颖,刘海涛..主成分分析在震动信号目标识别算法中的应用[J].华中科技大学学报：自然科学版,2012,40(7):24-28,5.基金项目
国家重大科技专项资金资助项目 （）
