Q-Learning-Based Noise Covariance Adaptation in Kalman Filter for MARG Sensors Attitude Estimation

The attitude estimation of a rigid body by magnetic, angular rate, and gravity (MARG) sensors is a research subject for a large variety of engineering applications. A standard solution for building up the observer is usually based on the Kalman filter and its different extensions for versatility and practical implementation. However, the performance of these observers has long suffered from the inaccurate process and measurement noise covariance matrices, which in turn entails tedious parameter turning procedures. To overcome the laborious noise covariance matrices regulation, we propose in this paper a Q-learning-based approach to autonomously adapt the values of process and measurement noise covariance matrices. The Q-learning method establishes a reinforcement learning mechanism that forces the noise covariance matrices pair with the least difference between predictions and measurements of output to be found in a predetermined candidate set of noise covariance matrices. The effectiveness of the Q-learning approach, applied to Extended Kalman filter-based attitude estimation, is validated through the Monte Carlo method that uses real flight data on an unmanned aerial vehicle.