Ultrasonic signal classification based on ambiguity plane feature

作     者：Du Xiuli Wang Yan Shen Yi 

作者机构：School of Information Engineering Dalian Univ. Dalian 116622 P. R. China School of Astronautics Harbin Inst. of Technology Harbin 150001 P. R. China 

出 版 物：《Journal of Systems Engineering and Electronics》 (系统工程与电子技术（英文版）)

年 卷 期：2009年第20卷第2期

页      面：427-433页

核心收录：

学科分类：080202[工学-机械电子工程] 08[工学] 080401[工学-精密仪器及机械] 0804[工学-仪器科学与技术] 080402[工学-测试计量技术及仪器] 0802[工学-机械工程] 

主　　题：ultrasonic testing signal classification ambiguity function K-L transform 

摘      要：Ambiguity function （AF） is proposed to represent ultrasonic signal to resolve the preprocessing problem of different center frequencies and different arriving times among ultrasonic signals for feature extraction, as well as offer time-frequency features for signal classification. Moreover, Karhunen-Loeve （K-L） transform is considered to extract signal features from ambiguity plane, and then the features are presented to probabilistic neural network （PNN） for signal classification. Experimental results show that ambiguity function eliminates the difference of center frequency and arriving time existing in ultrasonic signals, and ambiguity plane features extracted by K-L transform describe the signal of different classes effectively in a reduced dimensional space. Classification result suggests that the ambiguity plane features obtain better performance than the features extracted by wavelet transform （WT）.