基于鲁棒时变卡尔曼滤波估计的无人机视觉编队

Time-Varying Kalman Filter Estimation for Vision Based Unmanned Aerial Vehicle Formation Flight

针对一般多输入多输出不确定系统,提出一种基于鲁棒时变卡尔曼滤波的估计算法.该方法将时变卡尔曼滤波与自适应神经网络相结合,利用自适应神经网络克服外界非线性不确定因素,采用两个误差信号对其进行训练以提高估计精度,并对估计误差有界性进行证明.将该方法用于无人机视觉编队视线信息的状态估计,仿真结果表明该算法能够很好地估计不确定机动长机的加速度,实现了僚机对长机的有效跟踪.

A robust time-varying Kalman filter for a class of multi-input multi-output uncetain system is proposed. This method combines a time-varying Kaiman filter with an adaptive neural network. It can overcome nonlinear uncertainty with the adaptive neural network trained by two error signals. The method can improve approaching precision, and the boundedness of the estimation error is proven by the Lyapunov theory. The proposed method is used to design state estimation of leader in the unmanned aerial vehicle（UAV） formation flight. Simulation results show that the method can estimate acceleration of leader flying with uncertain maneuvers. The follower can effectively track the leader. Thus effectiveness of the method is validated.