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.

Aiming at the problem of reduced positioning accuracy of unmanned swarm navigation systems due to dynamic abrupt noise in a complex electromagnetic environment, this paper proposes an adaptive Kalman filtering positioning and calibration method based on dynamic mutation perception and collaborative correction. This method optimizes the performance of Kalman filtering by monitoring the mutation of acceleration and velocity in real time, designing a dynamic threshold detection mechanism, adaptively adjusting the covariance matrix, and using multidimensional scaling analysis to calculate the similarity of trajectories and collaboratively correct the current state. The experiment uses simulation and real scene data and compares algorithms such as the traditional extended Kalman filter to verify the effectiveness of the proposed method, providing an effective solution for the collaborative positioning of an unmanned swarm under complex electromagnetic interference.

To address the degradation in 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.