Abstract
To improve the dynamic random error compensation accuracy of the Micro Electro Mechanical System (MEMS) gyroscope at different angular rates, an adaptive filtering approach based on the dynamic variance model was proposed. In this paper, experimental data were utilized to fit the dynamic variance model which describes the nonlinear mapping relations between the MEMS gyroscope output data variance and the input angular rate. After that, the dynamic variance model was applied to online adjustment of the Kalman Filter measurement noise coefficients. The proposed approach suppressed the interference from the angular rate in the filtering results. Dynamic random errors were better estimated and reduced. Turntable experiment results indicated that the adaptive filtering approach compensated for the MEMS gyroscope dynamic random error effectively both in the constant angular rate condition and the continuous changing angular rate condition, thus achieving adaptive dynamic random error compensation.
Highlights
The Micro-Electro-Mechanical Systems (MEMS) gyroscope has advantages of affordable, compactness and low power consumption, which is widely used in the fields of inertial measurement and inertial stabilization with considerably good application prospects [1,2,3,4,5]
The author worked on Micro Electro Mechanical System (MEMS) gyroscope testing for many years, and it was found that statistical characteristics of gyroscope random error had a relation with angular rates [21,22]
The dynamic variance model Vω = f (ω ) between random signal variance Vω and angular rate ω were studied, which is the foundation of gyroscope random signal filtering
Summary
The Micro-Electro-Mechanical Systems (MEMS) gyroscope has advantages of affordable, compactness and low power consumption, which is widely used in the fields of inertial measurement and inertial stabilization with considerably good application prospects [1,2,3,4,5]. Sensors 2018, 18, 3943 output data has a relation to gyroscope motion [21,22] It is shown, that the statistic characteristics of gyroscope dynamic random errors have certain mapping relations with angular rate. References [23,24] applied the dynamic Allan variance method to the analysis ring laser gyroscope (RLG) and the MEMS gyroscope’s dynamic random error characteristics. Reference [25] utilized the Fading Kalman Filtering method to restrain the MEMS gyroscope random error. The author worked on MEMS gyroscope testing for many years, and it was found that statistical characteristics of gyroscope random error had a relation with angular rates [21,22].
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