Improving the Detection Accuracy of Wake Targets by Improved One-class Support Vector Machine

doi:10.3969/j.issn.1000-1093.2020.09.021

Abstract

Abstract: A turbulent area containing a large number of bubbles is generated at the tail of the ship during the voyage. The ship can be effectively tracked by the acoustic detection of wake. An improved one-class support vector machine (SVM) algorithm is proposed，which uses the echo signal in the wake-free case as an optimal classifier for the training set for judging the wake echo signal mode. The echo signal is denoised，and then an adaptive feature extraction method is proposed to process the echo signal; a dual-threshold one-class SVM with two-layer decision boundary is used for wake detection by using feature extraction as input. The simulated results show that，compared with the conventional one-class support vector machine，the improved algorithm can be used to improve the detection accuracy which is up to 96.27% under different SNRs.

Key words: ship, wakedetection, featureextraction, adaptivethreshold, one-classsupportvectormachine

CLC Number:

TB566

WANG Cheng， WU Yan， YANG Tingfei. Improving the Detection Accuracy of Wake Targets by Improved One-class Support Vector Machine[J]. Acta Armamentarii, 2020, 41(9): 1887-1893.

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