Abstract
As a widely used inertial device, a MEMS triaxial accelerometer has zero-bias error, nonorthogonal error, and scale-factor error due to technical defects. Raw readings without calibration might seriously affect the accuracy of inertial navigation system. Therefore, it is necessary to conduct calibration processing before using a MEMS triaxial accelerometer. This paper presents a MEMS triaxial accelerometer calibration method based on the maximum likelihood estimation method. The error of the MEMS triaxial accelerometer comes into question, and the optimal estimation function is established. The calibration parameters are obtained by the Newton iteration method, which is more efficient and accurate. Compared with the least square method, which estimates the parameters of the suboptimal estimation function established under the condition of assuming that the mean of the random noise is zero, the parameters calibrated by the maximum likelihood estimation method are more accurate and stable. Moreover, the proposed method has low computation, which is more functional. Simulation and experimental results using the consumer low-cost MEMS triaxial accelerometer are presented to support the abovementioned superiorities of the maximum likelihood estimation method. The proposed method has the potential to be applied to other triaxial inertial sensors.
Highlights
Nowadays, with the gradual rise of microelectromechanical system (MEMS), low-precision inertial sensors, especially low-cost inertial sensors, have been widely used in many fields, such as motion tracking, attitude-controlling system, and unmanned aircraft systems
A complete error model of MEMS triaxial accelerometers is constructed. e influence of biased noises of the traditional objective function model, which is constructed by modulus calculation, is eliminated
A calibration method based on the maximum likelihood (ML) estimation method is proposed in this paper. e total error unbiased objective function is constructed, and the optimal estimation is obtained by the Newton iteration method
Summary
Is paper presents a MEMS triaxial accelerometer calibration method based on the maximum likelihood estimation method. E error of the MEMS triaxial accelerometer comes into question, and the optimal estimation function is established. Compared with the least square method, which estimates the parameters of the suboptimal estimation function established under the condition of assuming that the mean of the random noise is zero, the parameters calibrated by the maximum likelihood estimation method are more accurate and stable. The proposed method has low computation, which is more functional. Simulation and experimental results using the consumer low-cost MEMS triaxial accelerometer are presented to support the abovementioned superiorities of the maximum likelihood estimation method. E proposed method has the potential to be applied to other triaxial inertial sensors
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