ArticleMicromachines2022
A Hybrid Algorithm for Noise Suppression of MEMS Accelerometer Based on the Improved VMD and TFPF.
Article in Micromachines, 2022. 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.
- Experimental Study on Temperature Compensation for Dual-Axis MEMS Accelerometers Using Adaptive Mode Decomposition and Hybrid Convolutional-Recurrent Temporal Network Modeling.Micromachines · 2025Article
- Temperature Compensation for MEMS Accelerometer Based on a Fusion Algorithm.Micromachines · 2024Article
- A Novel Joint Denoising Method for Hydrophone Signal Based on Improved SGMD and WT.Sensors (Basel, Switzerland) · 2024Article
- Temperature Drift Compensation for Four-Mass Vibration MEMS Gyroscope Based on EMD and Hybrid Filtering Fusion Method.Micromachines · 2023Article
- Research on a Method to Improve the Temperature Performance of an All-Silicon Accelerometer.Micromachines · 2023Article
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Authors and funding
3 authors.
Funding
Abstract
High-G MEMS accelerometer (HGMA) is a new type of sensor; it has been widely used in high precision measurement and control fields. Inevitably, the accelerometer output signal contains random noise caused by the accelerometer itself, the hardware circuit and other aspects. In order to denoise the HGMA's output signal to improve the measurement accuracy, the improved VMD and TFPF hybrid denoising algorithm is proposed, which combines variational modal decomposition (VMD) and time-frequency peak filtering (TFPF). Firstly, VMD was optimized by the multi-objective particle swarm optimization (MOPSO), then the best decomposition parameters [
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