ArticleMicromachines2021
Hybrid Deep Recurrent Neural Networks for Noise Reduction of MEMS-IMU with Static and Dynamic Conditions.
Article in Micromachines, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed, 77 citations in OpenAlex.
- Single-Axis Rotational Inertial Navigation Systems for USVs: A Review of Key Technologies.Micromachines · 2026Review
- Optimal Low-Cost MEMS INS/GNSS Integrated Georeferencing Solution for LiDAR Mobile Mapping Applications.Sensors (Basel, Switzerland) · 2025Article
- Camera, LiDAR, and IMU Spatiotemporal Calibration: Methodological Review and Research Perspectives.Sensors (Basel, Switzerland) · 2025Review
- A Method for Measuring the Error Rules in Visual Inertial Odometry Based on Scene Matching Corrections.Micromachines · 2024Article
- Temperature Compensation Method for Piezoresistive Pressure Sensors Based on Gated Recurrent Unit.Sensors (Basel, Switzerland) · 2024Article
- Flight Attitude Estimation with Radar for Remote Sensing Applications.Sensors (Basel, Switzerland) · 2024Article
- Identification and interpretation of gait analysis features and foot conditions by explainable AI.Scientific reports · 2024Article
- RIOT: Recursive Inertial Odometry Transformer for Localisation from Low-Cost IMU Measurements.Sensors (Basel, Switzerland) · 2023Article
- Application and Prospect of Artificial Intelligence Methods in Signal Integrity Prediction and Optimization of Microsystems.Micromachines · 2023Review
- MEMS Inertial Sensor Calibration Technology: Current Status and Future Trends.Micromachines · 2022Review
- A hybrid learning-based stochastic noise eliminating method with attention-Conv-LSTM network for low-cost MEMS gyroscope.Frontiers in neurorobotics · 2022Article
- Adaptive Navigation Algorithm with Deep Learning for Autonomous Underwater Vehicle.Sensors (Basel, Switzerland) · 2021Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Micro-electro-mechanical system inertial measurement unit (MEMS-IMU), a core component in many navigation systems, directly determines the accuracy of inertial navigation system; however, MEMS-IMU system is often affected by various factors such as environmental noise, electronic noise, mechanical noise and manufacturing error. These can seriously affect the application of MEMS-IMU used in different fields. Focus has been on MEMS gyro since it is an essential and, yet, complex sensor in MEMS-IMU which is very sensitive to noises and errors from the random sources. In this study, recurrent neural networks are hybridized in four different ways for noise reduction and accuracy improvement in MEMS gyro. These are two-layer homogenous recurrent networks built on long short term memory (LSTM-LSTM) and gated recurrent unit (GRU-GRU), respectively; and another two-layer but heterogeneous deep networks built on long short term memory-gated recurrent unit (LSTM-GRU) and a gated recurrent unit-long short term memory (GRU-LSTM). Practical implementation with static and dynamic experiments was carried out for a custom MEMS-IMU to validate the proposed networks, and the results show that GRU-LSTM seems to be overfitting large amount data testing for three-dimensional axis gyro in the static test. However, for
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