中文

Journal of Applied Sciences ›› 2011, Vol. 29 ›› Issue (5): 545-550.doi: 10.3969/j.issn.0255-8297.2011.05.016

• Control and System • Previous Articles    

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

LI Xue-song1, LI Ying-hui1, LI Xia1, WANG Zhi-ke2   

  1. 1. Engineering College, Air Force Engineering University, Xi’an 710038, China
    2. Department of Aviation Theory, Aviation University of Air Force, Changchun 130022, China
  • Received:2010-12-15 Revised:2011-03-30 Online:2011-09-28 Published:2011-09-30

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Abstract:

 A robust time-varying Kalman filter for a class of multi-input multi-output uncetain system is proposed. This method combines a time-varying Kalman 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.

Key words: robust time-varying Kalman filter, unmanned aerial vehicle(UAV), vision-based formation flight, adaptive neural network

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