Abstract
In view of the large output noise and low precision of the Micro-electro-mechanical Systems (MEMS) gyroscope, the virtual gyroscope technology was used to fuse the data of the MEMS gyroscope to improve its output precision. Random error model in the conventional virtual gyroscopes contained an angular rate random walk and angle random walk ignoring other noise items and the virtual gyroscope technology can not compensate all random errors of MEMS gyroscope. So, the improved virtual gyroscope technology based on the autoregressive moving average (ARMA) model was proposed. First, the conventional virtual gyroscope technology was used to model the random error of three MEMS gyroscopes, and the data fusion was carried out by a Kalman filter to get the output of the virtual gyroscope. After that, the ARMA model was used to model the output of the virtual gyroscope, the random error model was improved with the ARMA model, and the Kalman filter was designed based on the improved random error model for data fusion of the MEMS gyroscopes. The experimental results showed that the standard deviation of the output of the virtual gyroscope based on the ARMA model was 1.4 times lower than that of the conventional virtual gyroscope output.
Highlights
With the advantages of small volume and low cost, mechanical Systems (MEMS) gyroscopes are widely used in aviation, aerospace, and other navigation fields [1,2]
The conventional virtual gyroscope technology is used to model the random error of three MEMS gyroscopes, and the data fusion is carried out through Kalman filter, and the output of virtual gyroscope is obtained
The autoregressive moving average (ARMA) model is used to model the output of virtual gyroscope, and combine the ARMA model and conventional random error model to improve the
Summary
With the advantages of small volume and low cost, MEMS gyroscopes are widely used in aviation, aerospace, and other navigation fields [1,2]. The angular acceleration is modeled as a first order time correlation process, and the dynamic modeling of angular velocity is carried out to further improve the compensation precision of the dynamic virtual gyroscope [7]. Because it is difficult to model the true value of the angular rate in a dynamic state, the effect of the true value of angular rate is usually offset by the output difference of gyroscope, which causes the error of the Kalman filter equation to describe the actual problem, which leads to the lower compensation precision. The ARMA model and virtual gyroscope technology are combined, and an improved virtual gyroscope technology based on ARMA model is proposed This method can further improve the random error compensation ability of virtual gyroscope technology in static state. Based on the modified random error model, the Kalman filter is designed for data fusion to further compensate the residual drift in virtual gyroscope output. The feasibility of the proposed method is verified by experiments
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