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  1. (空军工程大学 电讯工程学院,陕西 西安 710077)
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2007-09-20 发布日期:2007-09-20

An improved unscented Kalman filter for nonlinear systems

PAN Ping-jun;FENG Xin-xi;LIU Ying-kun;SHI Lie;LI Zheng

  1. (School of Telecommunication Engineering, Air Force Engineering Univ., Xi’an, 710077, China)
  • Received:1900-01-01 Revised:1900-01-01 Online:2007-09-20 Published:2007-09-20

摘要: 作为最近提出的一种非线性滤波方法,Unscented卡尔曼滤波器(UKF)具有易实现、较高的估计精度和中等的计算量等优点。然而,像扩展卡尔曼滤波器(EKF)一样,UKF关于模型不确定性的鲁棒性很差、对初始条件很敏感,容易出现状态估计不准,甚至发散等现象。为了克服UKF的缺陷,基于强跟踪滤波器(STF)理论,通过引入一种多重次优渐消因子在线调整滤波器增益矩阵,提出一种改进的UKF,并通过目标跟踪的仿真实验验证了该滤波器的有效性。仿真实验结果表明改进的Unscented卡尔曼滤波器具有好的鲁棒性,而且能够快速收敛。

关键词: Unscented卡尔曼滤波器, 强跟踪滤波器, 非线性系统, 目标跟踪

Abstract: The significant advantages of the recently developed Unscented Kalman Filter (UKF) for nonlinear systems are its easly implementation, better accuracy and moderate computational complexity. However, the UKF has as bad robustness as the extended Kalman filter (EKF) in modelling uncertainty, and is sensitive to the initial conditions. To overcome the limitations of the UKF, an improved UKF based on the theory of Strong Tracking Filters (STF) is developed in the paper. The improved filter could adjust a filtering gain matrix on line by introducing a time-varied fading matrix. Its effectiveness is demonstrated using a simulation example of target tracking. The simulation results show that the improved UKF has good robustness and can rapidly converge.

Key words: Unscented Kalman Filter, Strong Tracking Filter, nonlinear systems, target tracking


  • TN713