Adaptive dynamic kalman filter for high-performance tilt-angles estimation

Abstract

Estimation of attitude is a potential task for autonomous vehicles as it directly affects the velocity and position estimates as well as the overall system autonomy. The estimation of attitude angles utilizing gyro measurements always suffers from increasing drift with time. Although accelerometer measurements can provide an absolute estimate of pitch and roll angles for static and low dynamics conditions, they have two drawbacks the effect of additional external acceleration and the inability to capture the high dynamics motion. In this paper, an adaptive Kalman filter (AKF) is utilized for integrating the gyros and accelerometer measurements for enhancing the roll and pitch angles estimates by compensating for the effect of linear acceleration as well as adaptively adapting the Kalman filter measurement covariance matrix. The proposed algorithm is evaluated using real offline measurements for a flying vehicle. Further, a comparative analysis is carried out with the original Kalman filter (KF) algorithm and with the imufilter from Sensor Fusion and Tracking Toolbox from Matlab. The results presented satisfactory performance and enhancement for roll and pitch angles estimate.