标题:Robust passive underwater acoustic detection method for propeller
作者:Song, Yongxing; Liu, Jingting; Cao, Linlin; Chu, Ning; Wu, Dazhuan
作者机构:[Song, Yongxing; Cao, Linlin; Chu, Ning; Wu, Dazhuan] Zhejiang Univ, Inst Proc Equipment, Coll Energy Engn, Hangzhou 310027, Zhejiang, Peoples R China 更多
通讯作者:Chu, Ning;Chu, N
通讯作者地址:[Chu, N]Zhejiang Univ, Inst Proc Equipment, Coll Energy Engn, Hangzhou 310027, Zhejiang, Peoples R China.
来源:APPLIED ACOUSTICS
出版年:2019
卷:148
页码:151-161
DOI:10.1016/j.apacoust.2018.12.024
关键词:Cyclostationary; Propeller; Passive underwater acoustic detection;; Principal component analysis; Low SNR
摘要:Characteristic amplitude modulated radiated noise signals are generated as the propeller operates underwater. Generally, passive underwater acoustic detection is accomplished by processing the radiated noise of a marine's propeller. This study proposes a robust passive underwater acoustic detection method of a propeller by combining cyclostationary mechanism and principal component analysis (PCA). The proposed method can extract the characteristic frequency information of a propeller accurately. The effectiveness of the proposed method is verified according to simulated signals and real application cases. Both monocomponent and multicomponent modulated simulated signals are used to test the performance of the proposed method. The proposed method is then applied to the acoustic signals of propeller experiments and merchant ship propeller. Finally, the superiority of the proposed method is demonstrated by comparing the application results with the kurtogram and cyclostationary analysis method, during the extraction of the characteristic frequency of the propeller under low signal-to-noise ratio (SNR). The innovation of the proposed method not only realizes marine propeller detection from propeller acoustic signals but also has remarkable performance under low SNR. (C) 2018 Elsevier Ltd. All rights reserved.
收录类别:EI;SCOPUS;SCIE
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85059145326&doi=10.1016%2fj.apacoust.2018.12.024&partnerID=40&md5=6eed67d97ebd355f5071b110ed43d99d
TOP