ArticleSensors (Basel, Switzerland)2019
MEMS Hydrophone Signal Denoising and Baseline Drift Removal Algorithm Based on Parameter-Optimized Variational Mode Decomposition and Correlation Coefficient.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Dynamic Hyperbolic Tangent PSO-Optimized VMD for Pressure Signal Denoising and Prediction in Water Supply Networks.Entropy (Basel, Switzerland) · 2025Article
- A Novel Joint Denoising Method for Hydrophone Signal Based on Improved SGMD and WT.Sensors (Basel, Switzerland) · 2024Article
- Study of Motion Sickness Model Based on fNIRS Multiband Features during Car Rides.Diagnostics (Basel, Switzerland) · 2023Article
- Utilizing SVD and VMD for Denoising Non-Stationary Signals of Roller Bearings.Sensors (Basel, Switzerland) · 2021Article
- A Direction-of-Arrival Estimation Algorithm Based on Compressed Sensing and Density-Based Spatial Clustering and Its Application in Signal Processing of MEMS Vector Hydrophone.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Underwater acoustic technology is an important means of detecting the ocean. Due to the complex influence of the marine environment, there is a lot of noise and baseline drift in the signals collected by hydrophones. In order to solve this problem, this paper proposes a denoising and baseline drift removal algorithm for MEMS vector hydrophone based on whale-optimized variational mode decomposition (VMD) and correlation coefficient (CC). Firstly, the power spectrum entropy (PSE), which reflects the variation characteristics of the signal frequency is selected as the fitness function of the whale-optimization algorithm to find the parameters (
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