A GNSS Integrity Augmentation System for Ground Vehicle Operations

The employment of GNSS for the navigation of autonomous ground vehicles has so far been applied to mining operations in Australia. Autonomous systems enable the development of navigation strategies such as global path-planning and path optimization for vehicle fleets, thereby lowering overall carbon emissions. Furthermore, autonomous ground vehicle operations can significantly improve safety ratings by eliminating human error arising from stress, fatigue and boredom. Widespread use of GNSS-based autonomous vehicles for ground operations is presently hindered by stringent safety regulations. This places strict integrity requirements on GNSS receivers, which must be able to detect GNSS signal errors and faults, and alert the navigation system in a timely manner. An integrity augmentation system is presented in this paper that can detect GNSS error sources and faults, and alert the navigation system of an autonomous ground vehicle in a timely manner. The system is developed by modelling GNSS error sources like antenna masking, signal attenuation and multipath and assigning threshold values for generating integrity alerts. The performance of the system in terms of GNSS fault detection is validated through a realistic simulation in a 3-D virtual ground environment. Trajectories representing the paths followed by vehicles are generated using a dynamic model of a generic fourwheeled ground vehicle. The integrity augmentation system was demonstrated to successfully detect GNSS errors and respond by issuing predictive (caution flags) and reactive (warning flags) in a timely manner for a range of trajectories and maneuvers

Aircraft dynamics model augmentation for RPAS navigation and guidance

An Aircraft Dynamics Model (ADM) augmentation scheme for Remotely Piloted Aircraft System (RPAS) navigation and guidance is presented. The proposed ADM virtual sensor is employed in the RPAS navigation system to enhance continuity and accuracy of positioning data in case of Global Navigation Satellite System (GNSS) data degradations/losses, and to improve attitude estimation by vision based sensors and Micro-Electromechanical System Inertial Measurement Unit (MEMS-IMU) sensors. The ADM virtual sensor is essentially a knowledge-based module that predicts RPAS flight dynamics (aircraft trajectory and attitude motion) by employing a rigid body 6-Degree of Freedom (6-DoF) model. Two possible schemes are studied for integration of the ADM module in the aircraft navigation system employing an Extended Kalman Filter (EKF) and an Unscented Kalman Filter (UKF). Additionally, the synergy between the navigation systems and an Avionics-Based Integrity Augmentation (ABIA) module is examined and a sensor-switching framework is proposed to maintain the Required Navigation Performance (RNP) in the event of single and multiple sensor degradations. The ADM performance is assessed through simulation of an RPAS in representative fight operations. Sensitivity analysis of the errors caused by perturbations in the input parameters of the aircraft dynamics is performed to demonstrate the robustness of the proposed approach. Results confirm that the ADM virtual sensor provides improved performance in terms of data accuracy/continuity, and an extension of solution validity time, especially when pre-filtered and employed in conjunction with a UKF