整合基于GAN的机器学习与非线性卡尔曼过,以增强状态估计
Lior Tobaly1, Eyal Yaniv2, Zeev Zalevsky3
1School of Business Administration, Bar-Ilan University, Ramat-Gan, 52900, Israel. lior.tobaly@biu.ac.il.
Scientific reports
|November 27, 2025
概括
这项研究通过将生成对抗网络 (GAN) 与无气味卡尔曼波器 (UKF) 集成,增强动态系统中的状态估计. 新的GAN-UKF方法动态调整过器参数,显著减少估计误差,以提高实时性能.
科学领域:
- 控制系统工程 控制系统工程
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 无气味卡尔曼波器 (UKF) 提供了比传统卡尔曼波器更好的非线性系统状态估计.
- UKF的性能受到静态参数的限制:工艺噪声共变率 (Q),测量噪声共变率 (R) 和缩放因子 (α, κ, β).
- 适应不断变化的系统动态对于准确的实时状态估计至关重要.
研究的目的:
- 开发一个新的框架,增强非线性动态系统中的状态估计.
- 使用生成对抗网络 (GAN) 实时动态调整UKF参数.
- 在复杂,不断变化的环境中提高状态估计的准确性和稳定性.
主要方法:
- 生成对抗网络 (GAN) 与无气味卡尔曼波器 (UKF) 的集成.
- 通过GAN实时预测和更新UKF静态参数 (Q,R,α, κ, β).
- 使用真实世界飞机导航数据 (位置,速度,方向,环境变量) 进行验证.
主要成果:
- 与静态模型相比,GAN增强的UKF显示了与静态模型相比,状态估计错误的显著减少.
- 动态参数调整使其能够更好地适应不断变化的系统动态.
- 提高了估计飞机导航状态的准确性.
结论:
- 拟议的GAN-UKF框架为非线性动态系统的状态估计提供了显著的进步.
- 动态参数适应是提高过器在不确定的和不断变化的环境中的性能的关键.
- 该框架可用于其他关键领域,如机器人,自动驾驶汽车和智能城市.
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