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Using Micro-Electro-Mechanical Systems MEMS to Develop Diagnostic Tools

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Published on: October 1, 2007

Signal Denoising Method Based on EEMD and SSA Processing for MEMS Vector Hydrophones.

Peng Wang¹, Jie Dong¹, Lifu Wang¹

¹School of Mathematics, North University of China, Taiyuan 030051, China.

|October 26, 2024

Summary

A new EEMD-SSA method effectively denoises micro-electromechanical system (MEMS) vector hydrophones by removing noise and drift. This combined approach improves signal utilization in underwater acoustic engineering applications.

Keywords:

ensemble empirical mode decomposition (EEMD)micro-electronic mechanical systems (MEMS) vector hydrophone singular spectrum analysis (SSA)

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Area of Science:

Underwater acoustics
Signal processing
Sensor technology

Background:

Micro-electromechanical system (MEMS) vector hydrophones are crucial in underwater acoustics but suffer from noise and drift.
Environmental noise and signal distortion complicate accurate data detection and recognition.

Purpose of the Study:

To develop an advanced denoising technique for MEMS vector hydrophones.
To enhance the utilization of received signals by mitigating noise and drift.

Main Methods:

A novel hybrid method combining Ensemble Empirical Mode Decomposition (EEMD) and Singular Spectrum Analysis (SSA) was developed.
Noise signals were decomposed using EEMD to identify and isolate intrinsic mode functions (IMFs) related to noise.
Singular Spectrum Analysis (SSA) was employed to reconstruct the signal, separating trend and periodic noise.

Main Results:

The proposed EEMD-SSA algorithm demonstrated superior denoising performance compared to using EEMD or SSA individually.
The method effectively removed both noise and signal drift, improving signal clarity.
Experimental validation using simulated and real-world lake test data confirmed the algorithm's practical value.

Conclusions:

The EEMD-SSA method offers a robust solution for denoising MEMS vector hydrophone signals.
This technique significantly enhances the quality of underwater acoustic data, enabling better signal detection and recognition.
The study highlights the practical research value of the EEMD-SSA algorithm in underwater acoustic engineering.