Field Sensor Bias Calibration with Angular-Rate Sensors: Theory and Experimental Evaluation with Application to Magnetometer Calibraion

Abstract

Field sensors, such as magnetometers and accelerometers, are widely used sensors for attitude estimation, yet their accuracy is limited by sensor measurement bias. This paper reports a novel methodology for estimating the sensor bias of three-axis field sensors. Our approach employs three-axis angular velocity measurements from an angular-rate gyroscope to estimate the three-axis field sensor measurement bias that, when properly calibrated, can significantly improve attitude estimation. We report three methods implementing this approach based on batch linear least squares, real-time Kalman filter, and real-time adaptive identification. Assuming the field is constant, our methods impose less restrictive conditions for the movements of the instrument required for calibration than previously reported methods, do not require a priori knowledge of the field (e.g., the magnitude of the local magnetic field) or the attitude of the instrument, and also ensure convergence for the estimated parameters. The proposed methods are evaluated and compared with the previously reported methods with numerical simulation and in a comparative laboratory and field experimental evaluation with the sensors onboard an underwater robot vehicle. Finally, as an application example of the magnetometer bias calibration, the proposed methods are used to improve the estimation of the position of an underwater vehicle in Monterey Bay at 2800 in depth.