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Inertial measurement units are widely used in different fields to estimatethe attitude. Many algorithms have been proposed to improve estimationperformance. However, most of them still suffer from 1) inaccurate initialestimation, 2) inaccurate initial filter gain, and 3) non-Gaussian processand/or measurement noise. In this paper, we leverage reinforcement learning tocompensate for the classical extended Kalman filter estimation, i.e., to learnthe filter gain from the sensor measurements. We also analyse the convergenceof the estimate error. The effectiveness of the proposed algorithm is validatedon both simulated data and real data.

Reinforcement Learning Compensated Extended Kalman Filter for Attitude Estimation