Gyroscope Denoising Algorithm Based on EMD-SSA-VMD Double-Layer Decomposition
Chuanqian Lv, Yaohong Zhao, Fangzhou Li, Haibo Luo
TL;DR
This paper introduces a new gyroscope noise reduction method combining EMD, VMD, and SSA to improve signal accuracy and precision.
Contribution
A novel dual-layer decomposition algorithm (EMD-SSA-VMD) for gyroscope signal denoising is proposed and validated.
Findings
The EMD-SSA-VMD algorithm improves SNR and reduces RMSE in simulated and real gyroscope data.
The method effectively separates noise from useful signals using entropy-based criteria and optimized decomposition.
Experimental results confirm the algorithm's practical effectiveness in engineering applications.
Abstract
To reduce random errors effectively and improve measurement precision in MEMS gyroscopes, we establish a dual-layer noise suppression method named EMD-SSA-VMD. The algorithm is grounded in empirical mode decomposition (EMD) and variational mode decomposition (VMD), incorporating the sparrow search algorithm (SSA) and entropy theory. The process starts by breaking down the signal into a series of intrinsic mode functions (IMFs) and a residual via EMD. By calculating the power spectral entropy (PSE) of IMFs, we can sort the signal components into three categories: noise signals, mixed signals, and effective signals. The mixed signals then undergo VMD processing, where SSA optimizes the key decomposition parameters. The sample entropy (SE) of the IMFs from VMD is computed to distinguish between actual signal components and noise. Finally, we combine all valuable signals to reconstruct the…
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Taxonomy
TopicsMachine Fault Diagnosis Techniques · Inertial Sensor and Navigation · Structural Health Monitoring Techniques