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.

Highlights

  • It is very important to find underwater targets as early as possible and extract their effective features for recognition, so as to take better defensive measures and countermeasures to reduce the threat of underwater targets, such as warships and submarines [1,2,3]

  • Combining the advantages of the above frequency feature extraction techniques, an improved line spectrum frequency feature extraction technique was put forward for underwater acoustic signals by using variational mode decomposition (VMD), duffing chaotic oscillator (DCO), and a kind of permutation entropy (PE) (KPE) in 2019 [29], which can accurately extract line spectrum frequency features of low-frequency intrinsic mode functions (IMFs). This frequency feature extraction technique still has some limitations: (i) the decomposition result of VMD is affected by parameter setting and (ii) KPE cannot reflect the amplitude information of time series, which is affects the accuracy of line spectrum frequency

  • This paper introduces a novel linear spectrum frequency feature extraction technique for warship radio noise based on CEEMDAN, DCO and weighted-permutation entropy (W-PE), named CEEMDAN-DCO-W-PF

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Summary

A Novel Linear Spectrum Frequency Feature

Extraction Technique for Warship Radio Noise Based on Complete Ensemble Empirical Mode.

Introduction
CEEMDAN
Linear Spectrum
CEEMDAN of Simulation
As in Figure
20 Hz corresponds toEMD
Linear
19.95 Hz and
Comparison
As in Table
CEEMDAN of Warship Radio Noise Signals
Linear Spectrum Frequency Feature Extraction of IMF10
Comparison of Frequency Feature Extraction Techniques
Asbased seen on in EMD
Findings
Conclusions
Full Text
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