A new adaptive immune clonal algorithm for underwater acoustic target sample selection

Author

Honghui Yang ; Xin Zhou ; Yun Wang ; Jian Dai ; Sheng Shen ; Jingyu Liu

Author_Institution

Dept. of Environ. Eng., Northwestern Polytech. Univ., Xi´an, China

fYear

2013

fDate

22-25 Oct. 2013

Firstpage

1

Lastpage

4

Abstract

The performance of underwater acoustic target classification decreases and is unstable when the training set contains noisy, redundant or irrelevant samples. In this paper, a new adaptive immune clonal sample selection algorithm (AICISA) is proposed to address this problem. AICISA is aimed at directing generation evolution. An experiment about the application of AICISA using the multi-field features extracted from 4 kinds of underwater acoustic targets was conducted. Experimental results show that AICISA can select effective subsets of samples. Reducing the sample size by 90%, the classification accuracy of SVM is improved by 10%. AICISA also shows good convergence and stability. The optimal subset of samples obtained by AICISA has good generalization ability and can remarkably reduce the classification time.

Keywords

acoustic signal detection; adaptive signal detection; signal classification; support vector machines; underwater acoustic communication; adaptive immune clonal sample selection algorithm; multifield features; support vector machines; training set; underwater acoustic target classification; underwater acoustic target sample selection; Accuracy; Classification algorithms; Cloning; Feature extraction; Signal processing algorithms; Support vector machines; Underwater acoustics; Adaptive immune clonal sample selection (AICISA); sample selection; support vector machines (SVM); underwater acoustic target classification;

fLanguage

English

Publisher

ieee

Conference_Titel

TENCON 2013 - 2013 IEEE Region 10 Conference (31194)

Conference_Location

Xi´an

ISSN

2159-3442

Print_ISBN

978-1-4799-2825-5

Type

conf

DOI

10.1109/TENCON.2013.6718810

Filename

6718810