Title :
Feature Extraction and Classification Based on Bispectrum for Underwater Targets
Author :
Yu, Haitao ; Wang, Yingmin ; Xie, Zhanlin ; Liu, Wei
Abstract :
According to the symmetric characteristics of bispectrum, a novel feature extraction scheme, which includes the summation-at-every-column feature vector, the summation-at-every-row feature vector and their combination in a triangle area, one of the 12 symmetric areas of bispectrum, is proposed. By using One-against-One (OAO) method of multi classification of Support Vector Machine (SVM), the mean classification accuracy for the radiated noise of underwater targets in three types is steadily above 98% for the summation-at-every-column feature vector and the combination feature vector respectively. The summation-at-every-row feature vector as a supplementary feature improves the classification performance but burdens the computation load of classification.
Keywords :
acoustic signal processing; feature extraction; signal classification; support vector machines; underwater sound; bispectrum; feature extraction scheme; one-against-one method; summation-at-every-column feature vector; summation-at-every-row feature vector; support vector machine; underwater target radiated noise; Accuracy; Equations; Feature extraction; Frequency domain analysis; Noise; Support vector machine classification; Support Vector Machine (SVM); bispectrum; classification; feature extraction; underwater targets;
Conference_Titel :
Intelligent System Design and Engineering Application (ISDEA), 2010 International Conference on
Conference_Location :
Changsha
Print_ISBN :
978-1-4244-8333-4
DOI :
10.1109/ISDEA.2010.310