超音速飞行体激波信号的主成分分析和K-均值聚类

提出一种基于激波信号的超音速飞行体的目标分类方法，通过5.56mm，7.62mm和12.7mm三种枪弹实测分析，提取信号的时域特征，用主成分分析法对信号的特征变量降维处理，用K-均值聚类算法进行聚类分析。对比直接用原始特征变量进行分类和经主成分分析处理后分类的效果，结果表明主成分分析的有效性和超音速目标分类识别的可行性。

Principal Component Analysis Based on Shock Wave from Supersonic Projectiles and K-means Algorithm

A classification technique based on shock wave from supersonic projectiles is proposed in this paper. Signal time-domain features is extracted. By experimental analysis for 5. 56 mm, 7. 62 mm and 12. 7 mm projectiles dimensions about signal feature variables is reduced with principal component analysis (PCA). K-means class assignments are used to class projectiles' classification. In comparision with the effect of classification clustered by original features and clustered after PCA, it is confirmed that PCA is effective and identifying supersonic projectiles based on shock wave signal is feasible.