A Kalman Filter-Based Localization Calibration Method Optimized by Reinforcement Learning and Information Matrix Fusion

To address the degradation of localization accuracy caused by insufficient robustness of filter parameters and inefficient multi-trajectory data fusion in dynamic environments, this paper proposes a Kalman filter-based localization calibration method optimized by reinforcement learning and information matrix fusion (RL-IMKF). An Actor-Critic reinforcement learning network is designed to adaptively adjust the state covariance matrix, enhancing the Kalman filter’s adaptability to environmental changes. Meanwhile, a multi-trajectory information matrix fusion strategy is introduced, which aggregates multiple trajectories in the information domain via weighted inverse covariance matrices to suppress error propagation and improve system consistency. Experiments using both simulated and real-world sensor data demonstrate that the proposed method outperforms traditional extended Kalman filter approaches in terms of localization accuracy and stability, providing a novel solution for cooperative localization calibration of Unmanned Aerial Vehicle (UAV) swarms in dynamic environments.