神经网络用于通过车载传感器融合来估计态度,视线和GNSS模糊性
1Aerospace Systems and Transport Research Group, Rey Juan Carlos University, 28942 Madrid, Spain.
Sensors (Basel, Switzerland)
|December 11, 2025
概括
本研究介绍了指导,导航和控制 (GNC) 系统的深度学习框架,将传感器数据融合为准确的态度,视线和全球导航卫星系统 (GNSS) 模糊性估计在具有挑战性的条件下.
科学领域:
- 航空航天工程 航空航天工程
- 机器学习 机器学习
- 导航系统 导航系统
背景情况:
- 准确的态度,视线 (LOS) 和全球导航卫星系统 (GNSS) 模两可的估计对于指导,导航和控制 (GNC) 系统至关重要.
- 传统方法通常需要精确的建模和高档传感器,在动态或不确定的环境中限制性能.
研究的目的:
- 研究一种基于深度学习的框架,用于估计态度,LOS和GNSS模糊性.
- 将来自车载传感器 (GNSS,IMU,SAL) 的数据与远程传感信息进行融合,以增强导航.
主要方法:
- 开发了两个神经网络估计器,用于GNSS载体相模糊性和重力向量的重建.
- 将基于学习的估计器集成到混合指导和导航方案中.
- 在六度自由度 (6-DoF) 模拟中验证了系统,使用炮火导弹和空气动力学建模.
主要成果:
- 基于学习的估计器捕获了非线性传感器状态关系,在退化条件下改进了概括.
- 与传统方法相比,拟议的系统减少了延迟,提高了估计准确度.
- 使用基于深度学习的传感器融合,证明了指导精度的提高.
结论:
- 基于深度学习的传感器融合为下一代低成本GNC系统提供了实际基础.
- 在有限的可观测性或传感器退化的情况下实现精确可靠的操作.
- 突出了先进AI在增强航空航天导航能力方面的潜力.
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