具有弹性多传感器无人机导航系统,采用混合联合融合架构
Sorin Andrei Negru1, Patrick Geragersian1, Ivan Petrunin1
1School of Aerospace, Transport, and Manufacturing, Cranfield University, Cranfield MK43 0AL, UK.
Sensors (Basel, Switzerland)
|February 10, 2024
概括
本研究引入了一种新的混合融合架构,用于在城市中无人驾驶飞行器 (UAV) 导航. 它提高了位置,导航和定时 (PNT) 的准确性和弹性,即使GPS信号丢失或城市干扰.
科学领域:
- 机器人技术和自主系统
- 导航和控制系统 导航和控制系统
- 传感器融合式传感器
背景情况:
- 城市无人机操作需要强大的定位,导航和定时 (PNT) 解决方案.
- 全球导航卫星系统 (GNSS) 的精度在城市地区因非视线 (NLOS) 和多路径效应而下降.
- 现有的融合架构在城市信号干扰下难以保持PNT准确性.
研究的目的:
- 开发和评估一种新的混合联合融合架构,用于城市环境中弹性无人机导航.
- 为应对GNSS多路径和复杂城市环境中的性能退化所带来的挑战.
- 确保在具有挑战性的条件下运行的无人机PNT数据的连续性和准确性.
主要方法:
- 一种混合联合融合架构,集成来自GNSS,惯性测量单元 (IMU),单眼相机和气压计的数据.
- 实施扩展卡尔曼波器 (EKFs) 作为局部波器和作为主波器的门式循环单元 (GRU) 块.
- 使用AirSim用于虚拟环境和Spirent GSS7000硬件用于真实世界的传感器数据进行数据收集.
主要成果:
- 拟议的混合融合架构与经典联合架构相比,显示出更好的弹性和稳定性.
- 该系统在恶劣条件下保持了良好的定位性能,其第95百分点误差为0.54米 (平方场景) 和1.72米 (调查场景).
- 该架构有效地缓解了GNSS多路径和性能恶化,包括在GNSS中断期间.
结论:
- 新的混合联合融合架构为城市环境中的无人机提供了强大而有弹性的PNT解决方案.
- 多个传感器和先进的过技术 (EKF,GRU) 的集成提高了导航准确性和可靠性.
- 这种方法为在复杂的,信号挑战的城市景观中可靠的无人机操作提供了途径.
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