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, a...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）.展开更多
The ambiguity function (AF) is proposed to represent the ultrasonic signal for its modulus’ independence of time shift and frequency shift, which avoids the effect of center frequency and arriving time of the ultraso...The ambiguity function (AF) is proposed to represent the ultrasonic signal for its modulus’ independence of time shift and frequency shift, which avoids the effect of center frequency and arriving time of the ultrasonic signal on feature extraction. Moreover, the K-L transform is considered to extract features from the ambiguity plane, and the effect of signals to noises on validity of ambiguity features is analyzed. Furthermore, we discuss the performance of recognizing ultrasonic signals at different center frequencies and different arriving time based on ambiguity features. Experimental results show that the features extracted by the K-L transform (KLT) are immune to noises, and can recognize ultrasonic signals effectively in a lower dimensional space.展开更多
文摘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）.
文摘The ambiguity function (AF) is proposed to represent the ultrasonic signal for its modulus’ independence of time shift and frequency shift, which avoids the effect of center frequency and arriving time of the ultrasonic signal on feature extraction. Moreover, the K-L transform is considered to extract features from the ambiguity plane, and the effect of signals to noises on validity of ambiguity features is analyzed. Furthermore, we discuss the performance of recognizing ultrasonic signals at different center frequencies and different arriving time based on ambiguity features. Experimental results show that the features extracted by the K-L transform (KLT) are immune to noises, and can recognize ultrasonic signals effectively in a lower dimensional space.
