Computer Science ›› 2017, Vol. 44 ›› Issue (9): 227-229.doi: 10.11896/j.issn.1002-137X.2017.09.042

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Research on Attitude Algorithm Based on Improved Extended Calman Filter

FENG Shao-jiang, XU Ze-yu, SHI Ming-quan and WANG Xiao-dong   

  • Online:2018-11-13 Published:2018-11-13

Abstract: In order to solve the problem that standard extended Kalman filter (EKF) in the multi-rotor UAV attitude solver has lower accuracy,an improved extended Kalman filter algorithm (BPNN-EKF) was proposed to improve the accuracy greatly.EKF prediction model parameters require the presence of priori known properties,but in engineering practice it is difficult to obtain accurate parameters.And nonlinear systems using linear model will cause error problem for standard EKF. Aiming at above problems,we used nonlinear mapping ability of neural network and adaptive ability to compensate the estimated value of the standard EKF,reduce the impact of the model as well as filtering parameters error for optimal estimates,thereby enhancing optimal estimation accuracy.The simulation results show that BPNN-EKF plays a significant role in improving multi-rotor UAV attitude solver accuracy.

Key words: Extended Kalman filter,Attitude algorithm,Nonlinear system,BP neural network

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