摘要
针对DARPA提出的全源导航需要快速集成并重新配置任意导航传感器的要求,结合民用无人机的任务需求,通过概率图模型相关原理,对基于因子图的无人机全源导航关键技术开展研究工作。采用因子图对系统状态更新过程进行表示,实现系统状态的递推与更新,完成传感器信息的数据综合。仿真结果表明,该方法能在传感器可用性时变的情况下,将不同传感器的数据进行有效融合,确保系统导航定位精度,使载体满足不断变化的任务需求与环境变化的要求。
The all source position navigation proposed by DARPA needs to integrate and reconfigure any navigation sensors quickly.According to the theory of probability graph model and the mission requirements of the civil unmanned aerial vehicle,the research on the key technology of all source position navigation based on factor graph was proposed.Factor nodes are used to express system state and measurement update procedure,and system state recursion and updates can be realized,and plug and play sensor information data synthesis can be completed.The simulation results show that the method can fuse the data of different sensors effectively,and make sure that the system navigation and positioning accuracy is guaranteed,so that the vehicle can satisfy changing task needs and environmental changes.
出处
《导航与控制》
2017年第2期1-5,共5页
Navigation and Control
基金
国家自然科学基金(编号:61533008
61328301)
关键词
无人机
因子图
全源导航
信息融合
unmanned aerial vehicle(UAV)
factor graph
all source position navigation
information fusion
