论文部分内容阅读
针对紊流风场环境下飞行速度因模型参数发生变化导致单一固定参数滤波器精度降低的问题,提出了一种无人机飞行状态多模型估计算法。在建立单一固定模型紊流风场有色噪声卡尔曼滤波器的基础上,采用多模型自适应卡尔曼估计,得到飞行速度的最优状态估计。仿真结果表明,多模型估计算法在模型参数发生变化时能有效地减小紊流风场对无人机飞行速度的影响,满足飞行速度控制输入的精度要求。
Aiming at the problem of reducing the accuracy of the single fixed parameter filter due to the change of the model parameters in the turbulent wind field environment, a multi-model estimation algorithm for the flight state of the UAV is proposed. Based on the built-in colored noise Kalman filter of turbulent wind field with single fixed model, the multi-model adaptive Kalman estimation is used to obtain the optimal state estimation of flight velocity. The simulation results show that the multi-model estimation algorithm can effectively reduce the influence of turbulence wind field on the flight speed of the UAV when the parameters of the model change, and meet the precision requirements of the flight speed control input.