A Fusion Frequency Feature Extraction Method for Underwater Acoustic Signal Based on Variational Mode Decomposition, Duffing Chaotic Oscillator and a Kind of Permutation Entropy

Electronics

Jing Yu¹  Xiaohui Yang²  Xiao Chen³  Yuxing Li⁴ 

[1] Information Engineering, ShaanXi University of Science &Technology, Xi’an 710021, China;;College of Electrical &Faculty of Information Technology and Equipment Engineering, Xi’an University of Technology, Xi’an 710048, China;

关键词: variational mode decomposition (VMD);    duffing chaotic oscillator (DCO);    permutation entropy (PE);    feature extraction;    frequency characteristic;    underwater acoustic signal;    ship-radiated noise;   

DOI  :  10.3390/electronics8010061

来源: DOAJ

【 摘 要 】

In order to effectively extract the frequency characteristics of an underwater acoustic signal under sensor measurement, a fusion frequency feature extraction method for an underwater acoustic signal is presented based on variational mode decomposition (VMD), duffing chaotic oscillator (DCO) and a kind of permutation entropy (PE). Firstly, VMD decomposes the complex multi-component underwater acoustic signal into a set of intrinsic mode functions (IMFs), so as to extract the estimated center frequency of each IMF. Secondly, the frequency of the line spectrum can be obtained by using DCO and a kind of PE (KPE). DCO is used to detect the actual frequency of the line spectrum for each IMF and KPE can determine the accurate frequency when the phase space track is in the great periodic state. Finally, the frequency characteristic parameters acted as the input of the support vector machine (SVM) to distinguish different types of underwater acoustic signals. By comparing with the other three traditional methods for simulation signal and different kinds of underwater acoustic signals, the results show that the proposed method can accurately extract the frequency characteristics and effectively realize the classification and recognition for the underwater acoustic signal.

【 授权许可】

Unknown