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Hierarchical refined composite multiscale fluctuation-based dispersion entropy: application to feature extraction of underwater target signal

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Abstract

Due to the complex and variable marine environment and weak target echo signals, the difficulty of extract underwater target features increases. On the basis of analyze the nonlinear and non-stationary characteristics of underwater target signals, in order to improve the detection performance of active sonar, the hierarchical refined multi-scale fluctuation-based dispersion entropy (HRCMFDE) method is proposed and used to extract the features of underwater target signals. Firstly, the HRCMFDE method is used to extract features from underwater target signals, and the extracted HRCMFDE feature vectors are used to form the feature set. Secondly, genetic algorithm (GA) was used to optimize the input weights and hidden layer bias of extreme learning machine (ELM), and an improved ELM algorithm was proposed to establish the recognition model for underwater target signals. Finally, a sample library of five types of targets was constructed based on the measured underwater target signals, and feature extraction was performed. The performance of the extracted features was tested through improved ELM classification, and the classification results obtained verified the effectiveness of the proposed feature extraction method.

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Data availability

The datasets generated during the current study are not publicly but are available from the corresponding author on reasonable request.

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Funding

Authors gratefully acknowledge the supported by National Natural Science Foundation of China (No. 11874302, No. 11574250 and No. 51179157) and Social Science Foundation of Shanxi (No. 2022M039).

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Authors and Affiliations

  1. School of Digital Arts, Xi’an University of Posts and Telecommunications, Xi’an, China

    Zhaoxi Li

  2. School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an, China

    Yaan Li

  3. Department of Computer and Information of Science and Engineering, University of Florida, Gainesville, FL, 32611, USA

    Kai Zhang

  4. School of Mathematics and Computer Science, Shaanxi University of Technology, Hanzhong, China

    Jianli Guo

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  1. Zhaoxi Li
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  2. Yaan Li
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  3. Kai Zhang
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Contributions

Zhaoxi Li designed the project and wrote the manuscript; Yaan Li, Kai Zhang and Jianli Guo help to revise the manuscript. All co-authors reviewed and approved the final manuscript.

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Correspondence to Zhaoxi Li or Yaan Li.

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Li, Z., Li, Y., Zhang, K. et al. Hierarchical refined composite multiscale fluctuation-based dispersion entropy: application to feature extraction of underwater target signal. Nonlinear Dyn 111, 22399–22417 (2023). https://doi.org/10.1007/s11071-023-09026-0

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  • DOI: https://doi.org/10.1007/s11071-023-09026-0

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