DocumentCode :
3442053
Title :
Classification of vessel targets using wavelet statistical features
Author :
Liu, Yihai ; Zhang, Xiaomin ; Yu, Yang
Author_Institution :
College of Marine, Northwestern Polytechnical University, Xi´an, China
fYear :
2012
fDate :
16-18 Oct. 2012
Firstpage :
1551
Lastpage :
1555
Abstract :
In this paper, a new algorithm of wavelet band-based feature extraction scheme is developed for usage in classifying underwater targets from the acoustic non-stationary signals. Based on the advantage of the wavelet transform (WT) in non-stationary signal processing, the algorithm extracts statistical features of the sequential data in each discrete wavelet frequency modulation band of the vessel radiated signals. Using a person-by-person optimization (PBPO) approach to select the target separability features from the line combination of the band-sequence zero cross density (BZD) features, the band-sequence variation degree (BVD) features and the band-sequence maxima density (BMD) features to create the final classification feature vectors. Theory analysis using distance function of the final feature vectors show that the optimal selected feature vector is effective. Experiment using two different type targets sea trial data show that this feature extraction scheme in underwater target classification is feasible and the recognition rate reaches 93.3%.
Keywords :
Non-stationary signal processing; Statistical feature extraction; Target classification; Wavelet transform;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image and Signal Processing (CISP), 2012 5th International Congress on
Conference_Location :
Chongqing, Sichuan, China
Print_ISBN :
978-1-4673-0965-3
Type :
conf
DOI :
10.1109/CISP.2012.6469636
Filename :
6469636
Link To Document :
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