A new SINS/GPS sensor fusion scheme for UAV localization problem using nonlinear SVSF with covariance derivation and an adaptive boundary layer  

作　　者：Fariz Outamazirt Fu Li Lin Yan Abdelkrim Nemra 

机构地区：[1]School of Automation Science and Electrical Engineering, Beihang University [2]School of Control and Automation, Ecole Militaire Polytechnique

出　　处：《Chinese Journal of Aeronautics》2016年第2期424-440,共17页中国航空学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(No.61375082)

摘　　要：The state estimation strategy using the smooth variable structure filter （SVSF） is based on the variable structure and sliding mode concepts. As presented in its standard form with a fixed boundary layer limit, the value of the boundary layer width is not precisely known at each step and may be selected based on a priori knowledge. The boundary layer width reflects the level of uncertainty in the model parameters and disturbance characteristics, where large values of the boundary layer width lead to robustness without optimality and small values of the boundary layer width provide optimality with poor robustness. As a solution and to overcome these limitations, an adaptive smoothing boundary layer is required to achieve greater robustness and suitable accuracy. This adapted value of the boundary layer width is obtained by minimizing the trace of the a posteriori covariance matrix. In this paper, the proposed new approach will be considered as another alternative to the extended Kalman filters （EKF）, nonlinear H∞ and standard SVSF-based data fusion techniques for the autonomous airborne navigation and self-localization problem. This alternative is based on strapdown inertial navigation system （SINS） and GPS data using the nonlinear SVSF with a covariance derivation and adaptive boundary layer width. Furthermore, the full mathematical model of the SINS/GPS navigation system considering the unmanned aerial vehicle （UAV） position, velocity and Euler angle as well as gyro and accelerometer biases will be used in this paper to estimate the airborne position and velocity with better accuracy.The state estimation strategy using the smooth variable structure filter （SVSF） is based on the variable structure and sliding mode concepts. As presented in its standard form with a fixed boundary layer limit, the value of the boundary layer width is not precisely known at each step and may be selected based on a priori knowledge. The boundary layer width reflects the level of uncertainty in the model parameters and disturbance characteristics, where large values of the boundary layer width lead to robustness without optimality and small values of the boundary layer width provide optimality with poor robustness. As a solution and to overcome these limitations, an adaptive smoothing boundary layer is required to achieve greater robustness and suitable accuracy. This adapted value of the boundary layer width is obtained by minimizing the trace of the a posteriori covariance matrix. In this paper, the proposed new approach will be considered as another alternative to the extended Kalman filters （EKF）, nonlinear H∞ and standard SVSF-based data fusion techniques for the autonomous airborne navigation and self-localization problem. This alternative is based on strapdown inertial navigation system （SINS） and GPS data using the nonlinear SVSF with a covariance derivation and adaptive boundary layer width. Furthermore, the full mathematical model of the SINS/GPS navigation system considering the unmanned aerial vehicle （UAV） position, velocity and Euler angle as well as gyro and accelerometer biases will be used in this paper to estimate the airborne position and velocity with better accuracy.

关 键 词：Adaptive smoothing bound-ary layer Autonomous airborne navi-gation GPS Smooth variable structurefilter （SVSF） Strapdown inertial naviga-tion system （SINS） 

分 类 号：V249[航空宇航科学与技术—飞行器设计] V279