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- W2918563609 abstract "Limited by the micro-electromechanical system (MEMS) fabrication technology, the in-operation drift of MEMS gyroscope which degrades measurement repeatability, accuracy, and stability has non-stationary wide-band components and a large difference between each power cycle. The drift limits the usability of MEMS gyroscope in a variety of field applications where autonomous and repeatable operation is required over a long time in harsh environmental conditions. Therefore, a novel method is proposed to compensate the drift. At first, an improved complete ensemble empirical mode decomposition is used to decompose the original signals into a series of intrinsic mode functions (IMFs), and the threshold de-noising method is adopted to filter the IMFs; then, the de-noised sub-series are reconstructed into training and testing dataset, respectively, and the bagging extreme learning machine-based model has been trained; finally, the compensation signal is predicted by the model with testing dataset, and the desired results can be obtained after compensation. The proposed method has been validated by a 9000-s in-operation experiment of CRG20 by comparing it with a typical method. The experiment demonstrated that the proposed method can enhance generalization performance and can boost compensation accuracy of the model, and the bias instability reduced from 0.0785°/s to 0.0046°/s." @default.
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- W2918563609 date "2019-07-01" @default.
- W2918563609 modified "2023-09-27" @default.
- W2918563609 title "The In-Operation Drift Compensation of MEMS Gyroscope Based on Bagging-ELM and Improved CEEMDAN" @default.
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- W2918563609 doi "https://doi.org/10.1109/jsen.2019.2902912" @default.
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