Abstract
It’s a difficult problem to filter MEMS gyroscope random noise in a dynamic state. Aiming at this problem, a method named EAKFA is proposed in this paper. The EAKFA method selects adaptive Kalman filter algorithm as a fading factor. Furthermore, the fading factor is optimized by evolutionary algorithms. Finally, the effectiveness of EAKFA is validated by comparing EAKFA with a general adaptive Kalman filter algorithm. The results show that the error mean and the standard deviation of the MEMS gyroscope are reduced by 2.5 % and 55.7 % after filtered, respectively.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China (No. 61305079), the open fund of State Key Laboratory of Software Engineering (No. SKLSE 2014-10-02), the Natural Science Foundation of Fujian Province of China under Grant (No. 2015J01235), the JK class project of Education Department of Fujian Province (JK2015006).
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Ni, Y., Ang, F., Du, X. (2015). Noise Reduction Method for MEMS Gyroscope Based on Evolved Adaptive Kalman Filter. In: Zhang, X., Wu, Z., Sha, X. (eds) Embedded System Technology. ESTC 2015. Communications in Computer and Information Science, vol 572. Springer, Singapore. https://doi.org/10.1007/978-981-10-0421-6_1
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DOI: https://doi.org/10.1007/978-981-10-0421-6_1
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