Aircraft dynamics model augmentation of GNSS based navigation and guidance systems for RPAS

This paper explores the benefits attainable by the adoption of an Aircraft Dynamics Model (ADM) to compensate for the shortcomings of employing low-cost sensors for high dynamics Remotely Piloted Aircraft System (RPAS) attitude determination tasks. Various low-cost sensors are considered for integration in the Navigation and Guidance System (NGS) including Global Navigation Satellite System (GNSS), vision based navigation and Micro- Electromechanical System Inertial Measurement Unit (MEMS-IMU) sensors. The ADM is essentially a knowledge-based module and is used to augment the navigation state vector by predicting the RPAS dynamics (aircraft trajectory and attitude motion). In the presented NGS, the ADM employs a six Degrees-of-Freedom (6- DoF) model. Multi-sensor data fusion is accomplished with a conventional Extended Kalman Filter (EKF) and an advanced Unscented Kalman Filter (UKF). After introducing the key mathematical models describing the 6-DoF ADM, the sensor and integrated system performance are compared in a small RPAS integration scheme (i.e., AEROSONDE RPAS platform) exploring a representative cross-section of the aircraft operational flight envelope and a preliminary sensitivity analysis is performed. In addition to a centralised filter, a dedicated ADM processor (i.e., a local pre-filter) is adopted to to extend the validity time of the virtual sensor across all segments of the RPAS trajectory. Simulation results corroborate the fact that ADM augmentation of GNSS based NGS provides improved performance in terms of attitude data accuracy and a significant extension of the operational validity time is achieved by pre-filtering.