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

为了改进基于震动信号的地面运动目标识别算法,提出了一种基于主成分分析(PCA)的2次特征提取算法.首先对地面运动目标引起的震动信号进行目标特性分析,提取多维的特征值;然后利用主成分分析方法对众多的特征值进行分析,去除特征值之间的相关性,提取综合特征值并应用于分类器,得到目标识别结果.基于实地采集的地面运动目标的震动信号进行实验,结果表明:该方法有效地减少了特征值的维数和相关性,降低了分类器训练的难度和训练时间,同时提高了目标的正确识别率.

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.