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An adaptive compensation algorithm for temperature drift of micro-electro-mechanical systems gyroscopes using a strong tracking Kalman filter.

Feng Y, Li X, Zhang X - Sensors (Basel) (2015)

Bottom Line: As the state variables of a strong tracking Kalman filter (STKF), the parameters of the temperature drift model can be calculated to adapt to the environment under the support of the compass.These parameters change intelligently with the environment to maintain the precision of the MEMS gyroscope in the changing temperature.The heading error is less than 0.6° in the static temperature experiment, and also is kept in the range from 5° to -2° in the dynamic outdoor experiment.

View Article: PubMed Central - PubMed

Affiliation: School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China. feng_yibo@163.com.

ABSTRACT
We present an adaptive algorithm for a system integrated with micro-electro-mechanical systems (MEMS) gyroscopes and a compass to eliminate the influence from the environment, compensate the temperature drift precisely, and improve the accuracy of the MEMS gyroscope. We use a simplified drift model and changing but appropriate model parameters to implement this algorithm. The model of MEMS gyroscope temperature drift is constructed mostly on the basis of the temperature sensitivity of the gyroscope. As the state variables of a strong tracking Kalman filter (STKF), the parameters of the temperature drift model can be calculated to adapt to the environment under the support of the compass. These parameters change intelligently with the environment to maintain the precision of the MEMS gyroscope in the changing temperature. The heading error is less than 0.6° in the static temperature experiment, and also is kept in the range from 5° to -2° in the dynamic outdoor experiment. This demonstrates that the proposed algorithm exhibits strong adaptability to a changing temperature, and performs significantly better than KF and MLR to compensate the temperature drift of a gyroscope and eliminate the influence of temperature variation.

No MeSH data available.


The heading error in simulation of SF.
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sensors-15-11222-f011: The heading error in simulation of SF.

Mentions: The heading error in the entire process is shown in Figure 11. The blue line is the heading error without SF compensation. The green line is the heading error which is compensated by SF with fixed parameters. The red line is the heading error which is compensated by the adaptive estimation of SF. The reason of the heading error spikes during rotation is that the sample frequencies of the MEMS gyroscope and FOG are different. Thus, a heading error spike will occur if the system has a high rotational speed. The adaptive algorithm estimated the suitable parameters of SF model, so that it could compensate the SF much more accurately than the fixed parameters methods.


An adaptive compensation algorithm for temperature drift of micro-electro-mechanical systems gyroscopes using a strong tracking Kalman filter.

Feng Y, Li X, Zhang X - Sensors (Basel) (2015)

The heading error in simulation of SF.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4481926&req=5

sensors-15-11222-f011: The heading error in simulation of SF.
Mentions: The heading error in the entire process is shown in Figure 11. The blue line is the heading error without SF compensation. The green line is the heading error which is compensated by SF with fixed parameters. The red line is the heading error which is compensated by the adaptive estimation of SF. The reason of the heading error spikes during rotation is that the sample frequencies of the MEMS gyroscope and FOG are different. Thus, a heading error spike will occur if the system has a high rotational speed. The adaptive algorithm estimated the suitable parameters of SF model, so that it could compensate the SF much more accurately than the fixed parameters methods.

Bottom Line: As the state variables of a strong tracking Kalman filter (STKF), the parameters of the temperature drift model can be calculated to adapt to the environment under the support of the compass.These parameters change intelligently with the environment to maintain the precision of the MEMS gyroscope in the changing temperature.The heading error is less than 0.6° in the static temperature experiment, and also is kept in the range from 5° to -2° in the dynamic outdoor experiment.

View Article: PubMed Central - PubMed

Affiliation: School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China. feng_yibo@163.com.

ABSTRACT
We present an adaptive algorithm for a system integrated with micro-electro-mechanical systems (MEMS) gyroscopes and a compass to eliminate the influence from the environment, compensate the temperature drift precisely, and improve the accuracy of the MEMS gyroscope. We use a simplified drift model and changing but appropriate model parameters to implement this algorithm. The model of MEMS gyroscope temperature drift is constructed mostly on the basis of the temperature sensitivity of the gyroscope. As the state variables of a strong tracking Kalman filter (STKF), the parameters of the temperature drift model can be calculated to adapt to the environment under the support of the compass. These parameters change intelligently with the environment to maintain the precision of the MEMS gyroscope in the changing temperature. The heading error is less than 0.6° in the static temperature experiment, and also is kept in the range from 5° to -2° in the dynamic outdoor experiment. This demonstrates that the proposed algorithm exhibits strong adaptability to a changing temperature, and performs significantly better than KF and MLR to compensate the temperature drift of a gyroscope and eliminate the influence of temperature variation.

No MeSH data available.