Solving the UAV localization problem using a Smooth Variable Structure Filtering

Recently a development of a new predictor-corrector filter based on sliding mode theory is proposed for state and parameter estimation known as the Smooth Variable Structure Filter (SVSF) which is robust and stable to modeling uncertainties making it suitable for Unmanned Aerial Vehicle (UAV) localization problem. Moreover, the SVSF is a robust recursive predictor-corrector estimation method that can effectively deal with uncertainties associated with initial conditions and modeling errors of SINS/GPS system. In contrast to the most mature filtering algorithms EKF and UKF the SVSF has for each state of the system that is being estimated, a set of performance indicators that correlate to the modeling uncertainties. In this paper the development and validation of an alternative robust SINS/GPS filtering scheme based on nonlinear SVSF for UAV localization problem is proposed to address the issues associated with the EKF and UKF filters which are largely used in navigation. The results obtained using the SVSF navigation filter are compared to the EKF and UKF navigation filters results using 3D UAV flight scenario. The comparison proves the efficiency and robustness of the UAV localization process using the SVSF.