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Open AccessArticle

A Hybrid Energy Feature Extraction Approach for Ship-Radiated Noise Based on CEEMDAN Combined with Energy Difference and Energy Entropy

by Yuxing Li 1,*, Xiao Chen 2 and Jing Yu 3
1
School of Information Technology and Equipment Engineering, Xi’an University of Technology, Xi’an 710048, China
2
College of Electrical & Information Engineering, ShaanXi University of Science & Technology, Xi’an 710021, China
3
School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China
*
Author to whom correspondence should be addressed.
Processes 2019, 7(2), 69; https://doi.org/10.3390/pr7020069
Received: 17 December 2018 / Revised: 26 January 2019 / Accepted: 27 January 2019 / Published: 1 February 2019
Influenced by the complexity of ocean environmental noise and the time-varying of underwater acoustic channels, feature extraction of underwater acoustic signals has always been a difficult challenge. To solve this dilemma, this paper introduces a hybrid energy feature extraction approach for ship-radiated noise (S-RN) based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) combined with energy difference (ED) and energy entropy (EE). This approach, named CEEMDAN-ED-EE, has two main advantages: (i) compared with empirical mode decomposition (EMD) and ensemble EMD (EEMD), CEEMDAN has better decomposition performance by overcoming mode mixing, and the intrinsic mode function (IMF) obtained by CEEMDAN is beneficial to feature extraction; (ii) the classification performance of the single energy feature has some limitations, nevertheless, the proposed hybrid energy feature extraction approach has a better classification performance. In this paper, we first decompose three types of S-RN into sub-signals, named intrinsic mode functions (IMFs). Then, we obtain the features of energy difference and energy entropy based on IMFs, named CEEMDAN-ED and CEEMDAN-EE, respectively. Finally, we compare the recognition rate for three sorts of S-RN by using the following three energy feature extraction approaches, which are CEEMDAN-ED, CEEMDAN-EE and CEEMDAN-ED-EE. The experimental results prove the effectivity and the high recognition rate of the proposed approach. View Full-Text
Keywords: complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN); energy difference (ED); energy entropy (EE); hybrid energy feature extraction; ship-radiated noise (S-RN) complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN); energy difference (ED); energy entropy (EE); hybrid energy feature extraction; ship-radiated noise (S-RN)
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Li, Y.; Chen, X.; Yu, J. A Hybrid Energy Feature Extraction Approach for Ship-Radiated Noise Based on CEEMDAN Combined with Energy Difference and Energy Entropy. Processes 2019, 7, 69.

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