Huang Zijia, Xu Qiushi, Sun Menghao, Zhu Xuzhen
National Key Laboratory of Multi-Domain Data Collaborative Processing and Control, Xi'an 710068, China.
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Entropy (Basel). 2025 Aug 1;27(8):821. doi: 10.3390/e27080821.
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.
为了解决动态环境中滤波器参数鲁棒性不足和多轨迹数据融合效率低下导致的定位精度下降问题，本文提出了一种基于卡尔曼滤波器的定位校准方法，该方法通过强化学习和信息矩阵融合（RL-IMKF）进行优化。设计了一种演员-评论家强化学习网络来自适应调整状态协方差矩阵，增强卡尔曼滤波器对环境变化的适应性。同时，引入了一种多轨迹信息矩阵融合策略，该策略通过加权逆协方差矩阵在信息域中聚合多个轨迹，以抑制误差传播并提高系统一致性。使用模拟和真实世界传感器数据进行的实验表明，所提出的方法在定位精度和稳定性方面优于传统的扩展卡尔曼滤波器方法，为动态环境中无人机群的协同定位校准提供了一种新的解决方案。
