A decision tree SVM classification method based on the construction of ship-radiated noise multidimension feature vector

Zhao Chen, Haiyan Wang, Xiaohong Shen, Jun Bai, Zhengguo Liu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

A decision tree support vector machine (SVM) classification method based on the construction of ship-radiated noise multidimension feature vector is proposed in this paper. Aimed at three kinds of ship targets (class I submarine, class II warship and class III merchant ship) radiated noise, the subband distribution feature vectors of their 1 1/2-spectrum and 2 1/2-spectrum, and scale-energy feature vector of them based on wavelet transform are constructed respectively. And then a 55-dimension comprehensive feature vector of the ship-radiated noise is constructed. On this basis, a 24-dimension feature vector is obtained by using K-L transform for feature optimization. Finally, support vector machine technique is applied for the classification and it enhances the classification accuracy.

Original languageEnglish
Title of host publication2011 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2011
DOIs
StatePublished - 2011
Event2011 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2011 - Xi'an, China
Duration: 14 Sep 201116 Sep 2011

Publication series

Name2011 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2011

Conference

Conference2011 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2011
Country/TerritoryChina
CityXi'an
Period14/09/1116/09/11

Keywords

  • classification
  • decision tree support vector machine
  • high-order spectrum
  • ship-radiated noise
  • wavelet transform

Fingerprint

Dive into the research topics of 'A decision tree SVM classification method based on the construction of ship-radiated noise multidimension feature vector'. Together they form a unique fingerprint.

Cite this