Adaptive Estimation of Measurement Bias in Three-Dimensional Field Sensors with Angular Rate Sensors: Theory and Comparative Experimental Evaluation

Three-axis magnetometers and three-axis 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 (e.g. magnetometers and accelerometers). 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. Our methods impose less restrictive conditions for the movements of the instrument required for calibration than previously reported methods, do not require knowledge of the direction of the field (e.g. 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 comparative experimental evaluation with sensors onboard an underwater robot vehicle.