通过强化学习和信息矩阵融合优化基于卡尔曼波器的本地化校准方法
Zijia Huang1, Qiushi Xu2, Menghao Sun2
1National Key Laboratory of Multi-Domain Data Collaborative Processing and Control, Xi'an 710068, China.
Entropy (Basel, Switzerland)
|August 28, 2025
概括
这项研究引入了一种使用强化学习和信息矩阵融合的新型卡尔曼波器方法,用于改进无人飞行器 (UAV) 群集定位. 这种方法提高了动态环境中的准确性和稳定性.
科学领域:
- 机器人技术
- 人工智能
- 控制系统
背景情况:
- 由于过参数退化和数据融合效率低下,动态环境给无人机群定位带来了挑战.
- 传统的本地化方法在复杂,不断变化的条件下难以适应和错误传播.
研究的目的:
- 在动态环境中开发基于卡尔曼波器的优化本地化校准方法.
- 通过自适应参数调整和先进数据融合来提高本地化准确性,稳定性和系统一致性.
主要方法:
- 建议采用基于卡尔曼波器的局部校准方法,将强化学习 (RL) 和信息矩阵融合 (IMKF) 整合起来.
- 演员关键RL网络可自适应地调整状态共变矩阵以提高卡尔曼波器的适应性.
- 多轨迹信息矩阵融合策略汇总信息领域的轨迹数据,以最大限度地减少错误的传播.
主要成果:
- 与传统的扩展卡尔曼波器 (EKF) 方法相比,提出的RL-IMKF方法显示出更高的定位精度和稳定性.
- 使用模拟和现实传感器数据的实验结果验证了RL-IMKF方法的有效性.
- 该方法显著改善了UAV群体在动态场景中的合作定位校准.
结论:
- 在具有挑战性的动态环境中,RL-IMKF方法为无人机群定位提供了强大而适应性的解决方案.
- 这项研究在合作本地化方面取得了重大进展,提高了多无人机系统的可靠性和性能.
- 改进RL的卡尔曼波器的适应性和融合策略是克服现有方法局限性的关键.
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