Feature Extraction and Classification of Spectrum of Radiated Noise of Underwater High Speed Vehicle

WANG Sen; WANG Yu; WANG Yichuan; LI Haitao

doi:10.11999/JEIT170283

Volume 39 Issue 11

Nov. 2017

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WANG Sen, WANG Yu, WANG Yichuan, LI Haitao. Feature Extraction and Classification of Spectrum of Radiated Noise of Underwater High Speed Vehicle[J]. Journal of Electronics & Information Technology, 2017, 39(11): 2684-2689. doi: 10.11999/JEIT170283

Citation:

WANG Sen, WANG Yu, WANG Yichuan, LI Haitao. Feature Extraction and Classification of Spectrum of Radiated Noise of Underwater High Speed Vehicle[J]. Journal of Electronics & Information Technology, 2017, 39(11): 2684-2689. doi: 10.11999/JEIT170283

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Feature Extraction and Classification of Spectrum of Radiated Noise of Underwater High Speed Vehicle

doi: 10.11999/JEIT170283

1.
(Navy Submarine Academy, Qingdao 266000, China)
2.
(PLA 31001, Beijing 100094, China)

Received Date: 2017-04-01
Rev Recd Date: 2017-08-25
Publish Date: 2017-11-19

Abstract

Abstract

In order to improve the result of underwater high speed vehicle classification, a classification method that is based on High Speed Characteristic Quantity (HSCQ) of vehicle radiated noise is designed. Firstly, analysis of Detection of Envelope Modulation On Noise (DEMON) spectrum of actual measured radiated noise is finished. The Modulation Distribution Ratio (MDR) of radiated noise is defined based on the separability of modulation frequency of DEMON spectrum. Then the spectrograms feature analysis and feature extraction of underwater high speed radiated noise are done based on image edge detection and edge growing. The Straight-line Characteristic Quantity of Spectrum (SCQS) of vehicle radiated noise is analyzed. Finally, considering the analysis results of two types of characteristic quantity, a new classification method of underwater high speed vehicle is realized and HSCQ of vehicle radiated noise is designed. The actual measured radiated noise analysis shows that, the false alarm rate of non-high speed vehicle is respectively 21.4% (only using MDR), 16.3% (only using SCQS), and 4.1% (using HSCQ).

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References(33)

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