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Research on Space-Based Gravitational Wave Signal Denoising Based on Improved VMD with Parrot Algorithm
Jingyi Xi1, Xiaolong Li1,2, Yunqing Liu1,2
1Institute of Electronic Information Engineering, Changchun University of Science and Technology, Changchun 130022, China.
Sensors (Basel, Switzerland)
|July 12, 2025
Summary
This study introduces a novel space-based method using Parrot algorithm and improved wavelet threshold to optimize variational mode decomposition for gravitational wave signal denoising. The new technique enhances signal quality and detection accuracy.
Area of Science:
- Astrophysics and Astronomy
- Signal Processing
- Data Analysis
Background:
- Gravitational wave (GW) detection is susceptible to noise, which can obscure signal characteristics.
- Effective noise reduction is crucial for accurate GW signal analysis and interpretation.
Purpose of the Study:
- To develop an advanced space-based denoising method for gravitational wave signals.
- To improve the signal-to-noise ratio (SNR) and detection accuracy of GW data.
- To provide a robust technical solution for extracting and analyzing faint GW signals.
Main Methods:
- A hybrid approach combining the Parrot algorithm (PO) and improved wavelet threshold (IWT) to optimize variational mode decomposition (VMD).
- PO is utilized for optimal selection of VMD parameters (number of modes K and penalty factor α).
- Modal components are classified, and IWT is applied for denoising noise components before signal reconstruction.
Main Results:
- The proposed method demonstrates superior performance in noise separation compared to existing algorithms.
- Significant improvement in the signal-to-noise ratio (SNR) of gravitational wave signals.
- Enhanced detection accuracy for gravitational wave events.
Conclusions:
- The integrated PO-IWT-VMD method offers a powerful new tool for gravitational wave signal processing.
- This technique effectively attenuates detector noise, preserving crucial waveform characteristics.
- The study advances the field of gravitational wave data analysis and extraction.
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