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Application Of Strongly Tracking Kalman Filter In MEMS Gyroscope Bias Compensation

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DOI: 10.23977/icamcs.2017.1004


Liu Hongdan, Shuxiong Ying, Li Xisheng

Corresponding Author

Liu Hongdan


In this paper, we make a research about a integrated system. The system consists of two main parts: micro-electro-mechanical systems (MEMS) gyroscopes and compass. In order to eliminate the temperature drift bias of MEMS gyroscope, we use a strong tracking Kalman filter and chooses an adaptive algorithm. We established a model about the bias of the temperature of the gyroscope. The parameters of the model are selected for the state variables to change intelligently with the temperature to increase the precision of the MEMS gyroscope[1-2]. In the static temperature experiment, the compensated heading error is less than 0.7°. We can draw a conclusion that the traditional Kalman filter compensation method, multiple linear regression compensation method, improved least squares method and strong tracking Kalman adaptive filtering algorithm all can compensate the gyroscope drift bias, but the adaptive filtering algorithm can be more accurate about the compensation of MEMS gyro drift bias, and eliminate the impact of temperature on its accuracy.


MEMS gyroscope, gyro bias, strong tracking adaptive Kalman filter, error compensation

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