Three-Axial Accelerometer Calibration Using Kalman Filter Covariance Matrix for Online Estimation of Optimal Sensor Orientation

Abstract

Inexpensive inertial/magnetic measurement units can be found in numerous applications and are typically used to determine orientation. Due to the presence of nonidealities in measurement systems, the calibration of the sensor is thus needed to determine sensor parameters such as bias, misalignment, and gain/sensitivity. In this paper, an online automatic calibration method for a three-axial accelerometer is presented. Parameters are estimated using an unscented Kalman filter. The sensor is placed in a number of different orientations using a robotic arm. These orientations are calculated online from the parameter covariance matrix and represent estimated optimal sensor orientations for parameter estimation. Numerous simulations are run to evaluate the proposed calibration method, and a comparison is made with an offline least mean squares calibration method. The simulation results show that calibration with the proposed method results in higher accuracy of parameter estimation when using less than 100 iterations. The proposed calibration method is also applied to a real accelerometer using a low number of iterations. The results show only slight (less than 0.4%) changes in parameter values between different calibration runs. The proposed calibration method provides an accurate parameter estimation using a small number of iterations without the need for manually predefining orientations of the sensor, and the method can be used in combination with other offline calibration methods to achieve even higher accuracy.