基于自适应联邦滤波的相对导航信息融合

Data fusion based on adaptive federal Kalman filter for relative navigation

针对基于GPS/MV组合导航方式的无人机空中加油问题,分析了对接阶段GPS及视觉传感器存在的条件约束。在建立导航传感器非线性相对位置测量模型的基础上,设计了基于扩展卡尔曼滤波的自适应联邦滤波器,并与集中式滤波进行了对比仿真。结果表明,提出的算法保证了部分传感器失效时导航数据输出的平稳性和容错性,滤波精度完全满足无人机空中加油相对导航系统要求。

According to the autonomous aerial refueling for UAVs based on GPS/Machine Vision integration navigation,the restrictions on the sensors during docking are analyzed.An adaptive federal Kalman filter is designed,which is based on extended Kalman filter algorithm,after modeling the sensors nonlinear relative position measurement models and further comparison is made between the proposed algorithms and centralized Kalman filter.The simulation shows that the outputs of the proposed algorithm are continuous and stabilized during sensor failure,and its precision can satisfy the requirements of UAV aerial refueling relative navigation system perfectly.