Abstract
Ship-radiated noise is one of the important signal types under the complex ocean background, which can well reflect physical properties of ships. As one of the valid measures to characterize the complexity of ship-radiated noise, permutation entropy (PE) has the advantages of high efficiency and simple calculation. However, PE has the problems of missing amplitude information and single scale. To address the two drawbacks, refined composite multi-scale reverse weighted PE (RCMRWPE), as a novel measurement technology of describing the signal complexity, is put forward based on refined composite multi-scale processing (RCMP) and reverse weighted PE (RWPE). RCMP is an improved method of coarse-graining, which not only solves the problem of single scale, but also improves the stability of traditional coarse-graining; RWPE has been proposed more recently, and has better inter-class separability and robustness performance to noise than PE, weighted PE (WPE), and reverse PE (RPE). Additionally, a feature extraction scheme of ship-radiated noise is proposed based on RCMRWPE, furthermore, RCMRWPE is combined with discriminant analysis classifier (DAC) to form a new classification method. After that, a large number of comparative experiments of feature extraction schemes and classification methods with two artificial random signals and six ship-radiated noise are carried out, which show that the proposed feature extraction scheme has better performance in distinguishing ability and stability than the other three similar feature extraction schemes based on multi-scale PE (MPE), multi-scale WPE (MWPE), and multi-scale RPE (MRPE), and the proposed classification method also has the highest recognition rate.
Highlights
With the development and application of modern sensor technology, it is possible to record time sequences accurately for further research [1,2], especially in the fields of resource detection, ocean, environmental monitoring, security, medical diagnosis, etc
All these resulRtsCiMnRdWicPaEteatnhdaMt tWhePEfecautruverse; e(ixi)ttrhaecStiToDnosfcWheGmNeubndaesreedacohncRurCveMisRsWmaPllEerathnadn one multi-scale WPE (MWPE) has a betteropf 1e/rffonromisae,nacnedtthhaenditfhfeereontche ebrettwweoenstchheeSmTDeso.fItnheotrwdoenrotiosefsuirntthheerRcCoMmRpWaPreE and their distinguishingMaWbPilEitcyuravnesdisstraelbatiilviteyly, lwaregec,awrhriychoius tinmlianenwy icthomthepcaornactliuvseionexthpaetrtihme ceonmtsploexnity of ship-radiated noise1c.a/nf noise is higher than that distinguish signals more of WGN; (iii) we can see that refined composite multi-scale reverse weighted PE (RCMRWPE) than the one of multi-scale PE (MPE) and multi-scale RPE (MRPE)
RCMRWPE is proposed by combining refined composite multi-scale processing (RCMP) and reverse weighted PE (RWPE), which aims to overcome the shortages of traditional multiscale distribution entropy in complexity measures of time series
Summary
With the development and application of modern sensor technology, it is possible to record time sequences accurately for further research [1,2], especially in the fields of resource detection, ocean, environmental monitoring, security, medical diagnosis, etc. In [26], a bearing multi-fault diagnosis method was put forward integrated EEMD, WPE, and improved SVM ensemble classifier, where WPEs of the first several intrinsic mode functions (IMFs) are served as the fault feature vectors of bearing vibration signals. PE ture extraction methods, the entropy-based feature extraction method can extract the complexity of the ship-radiated noise, and has better performance at distinguishing between different ships [14], such as PE, RPE, and WPE. Ripesfi[n1e4d], CsuocmhpaossPitEe,MRPuElt,i-aSncdalWe RPeEv.eIrnsethWisepigahpteerd, wPeerampuptlaytiRoCnMEnRtWroPpEy to the artificial rRanCdMoRmWsiPgEn,aals anndovaectlucaolmupnldeexritwyamteertraicco, uisstbiacsseidgnoanlsR, WanPdEparonpdoRseCManPu. nTdherwefaotreer, tahciosussetcictiosingnfiarlstfeinaturoreduexcetrsaRctWioPnEs,chanemd tehbeansepdreosnenRtsCRMCRMWRPWE.PAE luasrignegnRuCmMbeProonf ftehaetubraeseixstroafcRtiWonPaEn.d classification experiments for ship-radiated noise prove the superiority and effectiveness of the proposed feature extraction scheme. RIWn PthEe section, we introduce RCMRWPE in detail, and introduce the feature veaxeltcartraogFcretosinoranusamsfgocbhilveleoremwnoefst:afiemnadteucsrleeaqsesuxifetirncaacctetiisoonnYma=nedt{hycoil,daisb=saifs1iec,da2t,oio3nn, R...eCx,pMNeR}r,iWmitPecnEat,snreabsrpeeecrcaetricvroieenldys.torIuunctStteeodcvtiaeorsnif3Ly, the effectiveness of the proposed feature extraction scheme in artificial random signals and ship-radiated noise classificya1tiony.1S+eτction⋯4 sumy1m+(amr−iz1e)τs the total research work.
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