紊流风场下无人机飞行状态多模型估计算法

Multiple Model Estimation Algorithm of UAV State in Turbulent Flow

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。

Since the flight speed of the UAV can be disturbed by the varying of parameter m the turbulent flow, the filtering accuracy of single parameter will be decreased, a multiple model estimation is presented for the UAV flight state. Based on the colored-noise Kalman filter, this paper presents a multiple model adaptive Kalman algorithm to estimate the flight speed in the turbulent flow. The simulation results demonstrate that the multiple model estimator can improve the estimation accuracy of the flight speed in the turbulent flow when the parameter varies.