Design of Maritime Unmanned Detection System Based on Multi-mode Communication

Fang, Shuiliu; Hao, Ruiting; Zhang, Longgang; Peng, Jihui; Yu, Jinheng; Pan, Changming

doi:10.1109/cecit53797.2021.00088

Cited by 1 publication

(1 citation statement)

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“…The EMD decomposition [25] is effective in dealing with nonlinear and non-smooth data; however, the EMD decomposition is an empirical method and still has many problems. Vijaya Baskar et al used EMD for noise reduction of hydroacoustic signals [26], and the analysis of the results obtained showed that EMD decomposition is prone to modal mixing and boundary effects, so that the IMFs at different scales contain similar components and even lose physical meaning; Fang et al studied the application of wavelet transform in hydroacoustic signal processing for analysis and proposed an improved method based on wavelet threshold denoising [27], which outperforms other methods in terms of noise reduction; In 2022, Liu et al proposed a completely non-recursive VMD model [28]. Compared with the EMD algorithm, variable VMD has solid theoretical derivation ability and stronger robustness to data sampling and noise.…”

Section: Introductionmentioning

confidence: 99%

Whale sound signal denoising based on SVMD and improved wavelet thresholding

Zhang,

Bai,

Wen

et al. 2024

Meas. Sci. Technol.

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Cetaceans have formed a set of sonar systems that rely on acoustic waves for communication, positioning, and environmental recognition in their long-term evolution. This sonar system is characterized by strong anti-interference ability, high localization accuracy, and strong recognition ability. Effective denoising of cetacean sound is the first link in the process of applying cetacean signal analysis. For the problem of effective denoising of whale sound signals in complex underwater environments, a new denoising method based on successive variational mode decomposition (SVMD) and improved wavelet thresholding is proposed. Firstly, the noisy high-frequency intrinsic mode functions (IMFs) obtained by SVMD decomposition are sieved by the correlation coefficient method; then, these high-frequency components are subjected to improved wavelet thresholding for noise reduction; finally, the signal is reconstructed with the low-frequency IMFs. The simulation results show that the denoising method works well, and the signal-to-noise ratio is high and the root-mean-square error is low, which effectively preserves the important information of the original signal.

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Section: Introductionmentioning

confidence: 99%