A directional algorithm from single MEMS vector hydrophone based on polynomial fitting and real-time attitude compensation

Wu, Yuding; Zhang, Wendong; Wu, Daiyue; Zhu, Shan; Liu, Yan; Jia, Li; Zhang, Guojun

doi:10.1016/j.measurement.2024.114195

Cited by 1 publication

(1 citation statement)

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“…However, the complex and variable nature of the marine environment and the unnatural sound sources such as various ships and military sonar (Siddagangaiah et al , 2016) pose great challenges to the signal acquisition and information transmission of the feedphone. Marine ambient noise interferes with the signal quality of feedphones and brings about a qualitative decline, and it has also brought great difficulties in accurate underwater target location (Wu et al , 2024). Therefore, it is crucial to preprocess the original signal to remove noise (Jia et al , 2023; Shang et al , 2023; Wu et al , 2023).…”

Section: Introductionmentioning

confidence: 99%

Improved DOA estimation of MEMS vector hydrophone combined with CEEMDAN and wavelet transform for noise reduction

Chang,

Zhang,

Zhang

et al. 2024

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Purpose Ciliated microelectromechanical system (MEMS) vector hydrophones pick up sound signals through Wheatstone bridge in cross beam-ciliated microstructures to achieve information transmission. This paper aims to overcome the complexity and variability of the marine environment and achieve accurate location of targets. In this paper, a new method for ocean noise denoising based on improved complete ensemble empirical mode decomposition with adaptive noise combined with wavelet threshold processing method (CEEMDAN-WT) is proposed. Design/methodology/approach Based on the CEEMDAN-WT method, the signal is decomposed into different intrinsic mode functions (IMFs), and relevant parameters are selected to obtain IMF denoised signals through WT method for the noisy mode components with low sample entropy. The final pure signal is obtained by reconstructing the unprocessed mode components and the denoising component, effectively separating the signal from the wave interference. Findings The three methods of empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD) and CEEMDAN are compared and analyzed by simulation. The simulation results show that the CEEMDAN method has higher signal-to-noise ratio and smaller reconstruction error than EMD and EEMD. The feasibility and practicability of the combined denoising method are verified by indoor and outdoor experiments, and the underwater acoustic experiment data after processing are combined beams. The problem of blurry left and right sides is solved, and the high precision orientation of the target is realized. Originality/value This algorithm provides a theoretical basis for MEMS hydrophones to achieve accurate target positioning in the ocean, and can be applied to the hardware design of sonobuoys, which is widely used in various underwater acoustic work.

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