一种基于自构架模糊EKF的目标跟踪方法  

A Tracking Method Based on Self-constructing Fuzzy EKF

作　　者：刘志勇[1] 王阿利[1] 王小红[2] LIU Zhi-yong;WANG A-li;WANG Xiao-hong(Xianyang Vocational and Technical College,Xianyang,Shaanxi 712000,China;Shaanxi Provincial Party School of the CPC,Xi’an,Shaanxi 710061,China)

机构地区：[1]咸阳职业技术学院,陕西咸阳712000 [2]陕西省委党校,陕西西安710061

出　　处：《计算技术与自动化》2022年第3期14-20,共7页Computing Technology and Automation

基　　金：陕西高等教育改革研究项目(21GY056)。

摘　　要：针对无人机飞行轨迹跟踪定位问题,研究了一种基于自构架模糊EKF目标跟踪方法。首先根据无人机系统的动力学方程建立了无人机系统控制模型,并将无人机控制系统中存在的高频随机干扰信号视为观测干扰噪声。其次设计了自构架模糊扩展卡尔曼滤波,将EKF估计方差与实际观测方差间的误差作为自构架模糊系统的一个输入,通过自构架模糊系统辨识,自适应地减小模糊EKF系统估计误差,提高对无人机系统的跟踪精度。最后,通过实验仿真证明了该方法对无人机飞行轨迹跟踪的有效性。A self-constructing fuzzy extended Kalman filter(EKF) method is presented for the tracking and positioning of unmanned aerial vehicle(UAV) flight trajectory. In the proposed method, the control model of the UAV system is established according to the dynamic equation of UAV system firstly, moreover, the high frequency random interference signal in UAV control system is regarded as the observation interference noise. Secondly, the self-constructing fuzzy EKF system is designed to identify and estimate the error adaptively, and the error between EKF estimation variance and actual observation variance is taken as an input, and then the tracking accuracy of UAV system is improved. Finally, the simulation results show that the proposed strategy is effective.

关 键 词：自构架模糊系统 扩展卡尔曼滤波 系统辨识 无人机系统 定位跟踪 

分 类 号：V411.8[航空宇航科学与技术—航空宇航推进理论与工程]