Aircraft dynamic navigation for unmanned aerial vehicles

Aircraft Dynamic Navigation for Unmanned Aerial Vehicles A dissertation submitted in partial fulfilment for the degree of Doctor of Philosophy Lennon Cork B.Eng Aerospace Avionics Queensland University of Technology Australian Research Centre for Aerospace Automation School of Robotics and Aerospace Systems Science and Engineering Faculty Queensland University of Technology Brisbane, Australia May 5, 2014 Aircraft Dynamic Navigation for Unmanned Aerial Vehicles Keywords: Unmanned Aircraft Systems (UAS), Unscented Kalman Filtering (UKF), Inertial Navigation Systems (INS), Global Positioning System (GPS), GPS/INS Integration, Aircraft Dynamic Modelling (ADM), Aircraft Dynamic Navigation (ADN) c Copyright 2014 by Lennon R Cork All Rights Reserved To Leonard and Shirley Stevens QUT Verified Signature Abstract Aircraft navigation is a well established field which has seen considerable research and development over the last 50 years Of particular significance is the evolution of the low-cost, strapdown, Inertial Navigation System (INS), and their integration with the Global Positioning System (GPS) Combined, the two systems provide both an accurate and consistent estimate of the aircraft’s position, attitude and velocity; and for this reason GPS/INS navigation play a significant role in the development of Unmanned Aerial Vehicles (UAVs) This thesis describes the investigation of an Aircraft Dynamic Navigation (ADN) approach, which incorporates an Aircraft Dynamic Model (ADM) directly into the navigation filter of a fixed-wing aircraft of UAV The ADM provides the filter with information on the forces and moments acting on the aircraft as a result of the control surface actuation, and enables the filter to directly predict the system’s inertial sensor measurements The result is a dynamic, model aiding approach, that offers performance improvements over the standard GPS/INS solution ADMs have been applied to the navigation problem in the past, however each example presented in the literature has used simplified models and limited the scope to specific regions of the aircraft’s dynamics This is acceptable when the application is a runway approach or autonomous take-off, but neglects the complex interactions that occur in-between these stable modes and over the longer term This research investigates the ADN approach in a broader context vii viii Abstract than has been done in the past, in particular it covers multiple phases of flight and presents a robust analysis of filtering performance The primary contribution of this research is the formulation of a directly aided, loosely coupled, Unscented Kalman Filter (UKF) which incorporates a complex, non-linear, laterally and longitudinally coupled, ADM The use of the UKF provides an opportunity to integrate the ADM without the need for model linearisation that would otherwise be required by the Extended Kalman Filter (EKF) A significant constraint applied was the use of a sensor suite representing a typical UAV, consisting of a Global Positioning System (GPS) receiver and Inertial Measurement Unit (IMU), with the addition of an Electronic Compass (EC), and Air Data (AD) Pitot Static System In order to investigate the proposed approach, a detailed Monte-Carlo simulation environment was implemented This allowed for a large spectrum of the aircraft’s dynamics to be reviewed in the context of the navigation problem This is important as the dynamic model introduces additional sources of error, which may only become evident during specific modes of flight 100 random missions were simulated using this environment each with a one hour duration, totalling 36 × 106 data-points against which the ADN approach has been assessed The results demonstrated an 80% improvement, i.e reduction in estimate error, in the system’s attitude estimate, a 75% improvement in the velocity estimate, and a 50% improvement in the angular rate estimate compared to the standard GPS/INS approach The bias estimates of the accelerometers and rate gyros are improved by 30%, and the navigation filter was used to provide direct estimates of the aircraft’s rotational rates, accelerations and angular accelerations In addition, the filter was able to maintain an estimate of the background wind within 1m/s of the true value This was demonstrated to be 80% better than using the aircraft’s cross-track error, and in addition did not need to perform intentional wind-finding manoeuvres Acknowledgments I would like to thank my principle supervisor, the late Professor Rodney Walker, who passed in 2011 and was unable to see the completion of this research Rod was continually patient and encouraging throughout the course of this extended candidate, and this work owes a lot to his enthusiasm and dedication As the founding Director of the Australian Research Centre for Aerospace Automation (ARCAA), Rod was a driving force in the Australian UAS industry, he made a large number friends, and will be missed This research also owes thanks to Professor Duncan Campbell for taking over Rod’s supervisory role and for the years where he acted as associate supervisor, and to Dr Luis Mejas for taking on the role of associate supervisor Special thanks to Daniel Fitzgerald, Damien Dusha, Duncan Greer, Reece Clothier and the rest of my colleagues at the ARCAA; to Torsten Merz and the Autonomous Systems Laboratory (ASL) at the Commonwealth Science and Industry Research Organisation (CSIRO); and the Engineering team at Insitu Pacific To my friends and family, and most importantly to Christina who provided me with more support an encouragement than all others combined This research was supported by a QUT Postgraduate Research Award and BEE top-up scholarships, and was originally established under the Cooperative Research Centre for Satellite Systems (CRCSS) Computational (and/or data visualisation) resources and services used in this work were provided by the QUT, High Performance Computing (HPC) and Research Support Group ix B.4 Aircraft Dynamic Navigation (ADN) 189 bx error (m/s2 ) 0.02 −0.02 20 40 60 40 60 40 60 40 60 Time (min) by error (m/s2 ) 0.02 −0.02 20 Time (min) bz error (m/s2 ) 0.02 −0.02 20 Time (min) NEES 0 20 Time (min) Figure B.4-23: ADN accelerometer bias estimate errors (Simulation of 100) 190 Extended Results bp error (m/s2 ) 0.01 −0.01 20 40 60 40 60 40 60 40 60 Time (min) bq error (m/s2 ) 0.01 −0.01 20 Time (min) br error (m/s2 ) 0.01 −0.01 20 Time (min) NEES 0 20 Time (min) Figure B.4-24: ADN gyroscope bias estimate errors (Simulation of 100) B.5 Summary B.5 191 Summary This Appendix presented the extended set of filtering results and provides a detailed view of the filtering performance of the three approaches investigated in this thesis The results presented in Chapter are derived from the full set of simulation data and summarises the tables and figures in this Appendix Bibliography [1] AAI Shadow 200 Tactical Unmanned Aircraft System (TUAS) Product 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investigation of an Aircraft Dynamic Navigation. .. technology and navigation filtering techniques, and focuses on improving performance through a novel dynamic approach to aircraft navigation This approach is termed Aircraft Dynamic Navigation (ADN),