一种基于自构架模糊EKF的目标跟踪方法
    点此下载全文
引用本文:刘志勇 1 ,王阿利1,王小红2.一种基于自构架模糊EKF的目标跟踪方法[J].计算技术与自动化,2022,(3):14-20
摘要点击次数: 182
全文下载次数: 0
作者单位
刘志勇 1 ,王阿利1,王小红2 (1.咸阳职业技术学院,陕西 咸阳 7120002.陕西省委党校陕西 西安 710061) 
中文摘要: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。
中文关键词: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
 
A Tracking Method Based on Self-constructing Fuzzy EKF
Abstract: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.
keywords:self-constructing fuzzy system  extended Kalman filter (EKF)  system identification  unmanned aerial vehicle (UAV)  tracking and positioning
查看全文   查看/发表评论   下载pdf阅读器