Autonomous Vehicle Dynamic Model-Based Navigation for Small UAVs

This paper presents a novel approach to autonomous navigation for small UAVs, with no extra sensor added to the conventional INS/GNSS setup. The proposed method significantly increases the accuracy and reliability of autonomous navigation, especially for small UAVs with low-cost IMUs. This improvement is of special interest in the case of GNSS outages, where inertial coasting drifts very quickly. In the proposed architecture, the VDM provides the estimate of position, velocity, and attitude, which is updated within a filter based on available observations, such as IMU data or when available, GNSS measurements. The filter is capable of estimating wind velocity and dynamic model parameters, in addition to navigation states and IMU sensor errors. Monte Carlo simulations reveal major improvements in navigation accuracy compared to conventional INS/GNSS systems during GNSS outages of 5 min. A discussion on the observability is also presented at the end. Copyright (C) 2016 Institute of Navigation.