Ultrasonic signal classification based on ambiguity plane feature  

作　　者：Du Xiuli Wang Yan Shen Yi 

机构地区：[1]School of Information Engineering, Dalian Univ., Dalian 116622, P. R. China [2]School of Astronautics, Harbin Inst. of Technology, Harbin 150001, P. R. China

出　　处：《Journal of Systems Engineering and Electronics》2009年第2期427-433,共7页系统工程与电子技术（英文版）

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）.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）.

关 键 词：ultrasonic testing signal classification ambiguity function K-L transform 

分 类 号：TN911.6[电子电信—通信与信息系统] TP242[电子电信—信息与通信工程]