A Novel Linear Spectrum Frequency Feature Extraction Technique for Warship Radio Noise Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, Duffing Chaotic Oscillator, and Weighted-Permutation Entropy

Yuxing Li; Long Wang; Xueping Li; Xiaohui Yang

doi:10.3390/e21050507

A Novel Linear Spectrum Frequency Feature Extraction Technique for Warship Radio Noise Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, Duffing Chaotic Oscillator, and Weighted-Permutation Entropy

Entropy (Basel). 2019 May 18;21(5):507. doi: 10.3390/e21050507.

Authors

Yuxing Li¹, Long Wang², Xueping Li¹, Xiaohui Yang³

Affiliations

¹ Faculty of Information Technology and Equipment Engineering, Xi'an University of Technology, Xi'an 710048, Shaanxi, China.
² School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710072, Shaanxi, China.
³ School of Art and Design, Inner Mongolia University of Science & Technology, Baotou 014010, Inner Mongolia, China.

Abstract

Warships play an important role in the modern sea battlefield. Research on the line spectrum features of warship radio noise signals is helpful to realize the classification and recognition of different types of warships, and provides critical information for sea battlefield. In this paper, we proposed a novel linear spectrum frequency feature extraction technique for warship radio noise based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), duffing chaotic oscillator (DCO), and weighted-permutation entropy (W-PE). The proposed linear spectrum frequency feature extraction technique, named CEEMDAN-DCO-W-PE has the following advantages in comparison with other linear spectrum frequency feature extraction techniques; (i) as an adaptive data-driven algorithm, CEEMDAN has more accurate and more reliable decomposition performance than empirical mode decomposition (EMD) and ensemble EMD (EEMD), and there is no need for presetting parameters, such as decomposition level and basis function; (ii) DCO can detect the linear spectrum of narrow band periodical warship signals by way of utilizing its properties of sensitivity for weak periodical signals and the immunity for noise; and (iii) W-PE is used in underwater acoustic signal feature extraction for the first time, and compared with traditional permutation entropy (PE), W-PE increases amplitude information to some extent. Firstly, warship radio noise signals are decomposed into some intrinsic mode functions (IMFs) from high frequency to low frequency by CEEMDAN. Then, DCO is used to detect linear spectrum of low-frequency IMFs. Finally, we can determine the linear spectrum frequency of low-frequency IMFs using W-PE. The experimental results show that the proposed technique can accurately extract the line spectrum frequency of the simulation signals, and has a higher classification and recognition rate than the traditional techniques for real warship radio noise signals.

Keywords: complete EEMD with adaptive noise (CEEMDAN); duffing chaotic oscillator (DCO); empirical mode decomposition (EMD); frequency feature extraction; linear spectrum; underwater acoustic signal; warship radio noise; weighted-permutation entropy (W-PE).

Abstract

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