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Communication Dans Un Congrès Année : 2022

A Dynamic Grid-based Q-learning for Noise Covariance Adaptation in EKF and its Application in Navigation

Résumé

The process and measurement noise covariance matrices significantly impact the Extended Kalman Filter (EKF) performance and are often hand-tuned in practice, which usually entails a tedious task. Q-learning, a well-known method in reinforcement learning, has been applied recently to better adapt the noise covariance matrices for EKF thanks to its simplicity and capability in handling uncertain environments. Typically, some heuristics are involved in designing the Q-learning-based EKF (QLEKF), such as the tuning of grid size and covariance matrices values of each state, which inevitably degrade the estimation performance when the heuristics are not suitable. We propose a dynamic grid-based Q-learning EKF (DG-QLEKF) to overcome that drawback, which brings two novelties, an updated epsilon-greedy algorithm and a dynamic grid strategy. The proposed algorithm and strategy can thoroughly exploit arbitrary search scope and find appropriate values of noise covariance matrices. The effectiveness of DG-QLEKF, applied in navigation for attitude and bias estimation, is validated through the Monte Carlo method and real flight data from an unmanned aerial vehicle. The DG-QLEKF leads to much more improved state estimation than the QLEKF and traditional EKF.
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Dates et versions

hal-03781984 , version 1 (09-11-2022)

Identifiants

Citer

Xiang Dai, Hassen Fourati, Christophe Prieur. A Dynamic Grid-based Q-learning for Noise Covariance Adaptation in EKF and its Application in Navigation. CDC 2022 - 61st IEEE Conference on Decision and Control, Dec 2022, Cancún, Mexico. ⟨10.1109/CDC51059.2022.9993410⟩. ⟨hal-03781984⟩
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