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Correlation network from multivariate time series: a new method for characterizing nonlinear dynamic behavior in marine acoustic signal

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Abstract

Marine acoustic signal detection is significant as it constitutes potential real-time monitoring of severe underwater threats. There is still a lack of an efficient approach to achieve weak acoustic signal detection with nonparametric and non-information priors. During the last decade, the complex network has emerged as a new multidisciplinary methodology for characterizing complex systems. It does not need to obtain information priors to analyze time series, which makes the marine acoustic signal detection of non-information priors possible. However, most existing research on complex networks focuses on noiseless ideal univariate time series. Further research needs to be conducted to extend complex network reconstruction from univariate time series to multivariate time series under a noise background. For this purpose, this paper proposes an algorithm to convert multivariate time series into an undirected complex network termed the correlation network method. Meanwhile, to realize the effective representation of the complex network, we further study the spectral characteristics. The correlation network method and the investigation of the spectral characteristics are demonstrated by simulation and applied to actual recorded data. The results indicate that the correlation network and graph spectral domain methods can effectively characterize nonlinear dynamic behavior in marine acoustic signals.

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Data availability statement

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This study was supported by the National Natural Science Foundation of China (key projects) (Grant No. 62031021) and National Natural Science Foundation of China (Grant Nos. 61671386, 61901385).

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Authors and Affiliations

  1. School of Marine Science and Technology, Northwestern Polytechnical University, 127 Youyi West Road, Beilin District, Xi’an, 710072, Shaanxi, China

    Hongwei Zhang, Yongsheng Yan & Chao Wang

  2. Key Laboratory of Ocean Acoustics and Sensing (Northwestern Polytechnical University), Ministry of Industry and Information Technology, 127 Youyi West Road, Beilin District, Xi’an, 710072, Shaanxi, China

    Hongwei Zhang, Haiyan Wang, Yongsheng Yan & Chao Wang

  3. School of Electronic Information and Artificial Intelligence Shaanxi University of Science and Technology, Weiyang University Park, Northern Suburb, Xi’an, 710021, Shaanxi, China

    Haiyan Wang

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  1. Hongwei Zhang
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Correspondence to Haiyan Wang.

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No conflict of interest exists in the submission of this manuscript, and all authors approved the manuscript publication. I want to declare on behalf of my co-authors that the work described was original research that has not been published previously and is not under consideration for publication elsewhere, in whole or in part. All the authors listed have approved the manuscript that is enclosed.

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Zhang, H., Wang, H., Yan, Y. et al. Correlation network from multivariate time series: a new method for characterizing nonlinear dynamic behavior in marine acoustic signal. Nonlinear Dyn 111, 13201–13214 (2023). https://doi.org/10.1007/s11071-023-08532-5

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