Abstract
Sensing underwater information has become particularly important to obtain information about the marine environment and target characteristics. At present, most interference models for underwater information sensing tasks under substantial interference choose Gaussian noise models. However, it often contains a strong impact and does not conform to the Gaussian distribution. Moreover, in the current research on the sensing of underwater unknown frequency signals, there are problems that the sensing method cannot sufficiently estimate the parameters of the unknown frequency signal, and the signal-to-noise ratio threshold is too high. An underwater environment sensing method is proposed by using the Lévy noise model to describe the underwater natural environment interference and estimate its parameters, which can better describe the impact characteristics of the underwater environment. Then, the intermittent chaos theory and variable step method are leveraged to improve the existing dual-coupled Duffing oscillator method. The simulation results show that the proposed method can sense weak signals in the background of strong Lévy noise and estimate its frequency, with an estimation error as low as 0.1%. Compared with the original one, the minimum signal-to-noise ratio threshold is reduced by 3.098 dB, and the computational overhead is significantly reduced.
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Acknowledgement
This work was supported in part by the National Natural Science Foundation of China (No. 62072074, No. 62076054, No. 62027827, No. 61902054), the Frontier Science and Technology Innovation Projects of National Key R&D Program (No.2019QY1405), the Sichuan Science and Technology Innovation Platform and Talent Plan (No. 2020JDJQ0020), the Sichuan Science and Technology Support Plan (No. 2020YFSY0010), and the Natural Science Foundation of Guangdong Province (No. 2018A030313354).
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Zhang, H., Qin, Z., Chen, D. (2021). Underwater Information Sensing Method Based on Improved Dual-Coupled Duffing Oscillator Under Lévy Noise Description. In: Gao, H., Wang, X. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 406. Springer, Cham. https://doi.org/10.1007/978-3-030-92635-9_10
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DOI: https://doi.org/10.1007/978-3-030-92635-9_10
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