ArticleSensors (Basel, Switzerland)2018
Self-Alignment MEMS IMU Method Based on the Rotation Modulation Technique on a Swing Base.
Article in Sensors (Basel, Switzerland), 2018. 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.
- A Scheme for System Error Calibration and Compensation of the Initial State of MEMS Inertial Navigation.Sensors (Basel, Switzerland) · 2025Article
- An Initial Alignment Technology of Shearer Inertial Navigation Positioning Based on a Fruit Fly-Optimized Kalman Filter Algorithm.Computational intelligence and neuroscience · 2020Article
- An Onsite Calibration Method for MEMS-IMU in Building Mapping Fields.Sensors (Basel, Switzerland) · 2019Article
- Novel Drift Reduction Methods in Foot-Mounted PDR System.Sensors (Basel, Switzerland) · 2019Article
- Research on an Improved Method for Foot-Mounted Inertial/Magnetometer Pedestrian-Positioning Based on the Adaptive Gradient Descent Algorithm.Sensors (Basel, Switzerland) · 2018Article
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Authors and funding
6 authors.
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Abstract
The micro-electro-mechanical-system (MEMS) inertial measurement unit (IMU) has been widely used in the field of inertial navigation due to its small size, low cost, and light weight, but aligning MEMS IMUs remains a challenge for researchers. MEMS IMUs have been conventionally aligned on a static base, requiring other sensors, such as magnetometers or satellites, to provide auxiliary information, which limits its application range to some extent. Therefore, improving the alignment accuracy of MEMS IMU as much as possible under swing conditions is of considerable value. This paper proposes an alignment method based on the rotation modulation technique (RMT), which is completely self-aligned, unlike the existing alignment techniques. The effect of the inertial sensor errors is mitigated by rotating the IMU. Then, inertial frame-based alignment using the rotation modulation technique (RMT-IFBA) achieved coarse alignment on the swing base. The strong tracking filter (STF) further improved the alignment accuracy. The performance of the proposed method was validated with a physical experiment, and the results of the alignment showed that the standard deviations of pitch, roll, and heading angle were 0.0140 ° , 0.0097 ° , and 0.91 ° , respectively, which verified the practicality and efficacy of the proposed method for the self-alignment of the MEMS IMU on a swing base.
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