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Aircraft Dynamics Model Augmentation for RPAS Navigation and Guidance

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Abstract

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.

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References

  1. Kanistras, K., Martins, G., Rutherford, M.J., Valavanis, K.P.: Survey of unmanned aerial vehicles (UAVs) for traffic monitoring. In: Handbook of Unmanned Aerial Vehicles, pp. 2643–2666. Springer (2015)

  2. Gonzalez, L.F., Montes, G.A., Puig, E., Johnson, S., Mengersen, K., Gaston, K.J.: Unmanned aerial vehicles (UAVs) and artificial intelligence revolutionizing wildlife monitoring and conservation. Sensors 16, 97 (2016)

    Article  Google Scholar 

  3. Yuan, C., Liu, Z., Zhang, Y.: UAV-based forest fire detection and tracking using image processing techniques. In: 2015 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 639–643 (2015)

  4. Gómez-Candón, D., De Castro, A., López-Granados, F.: Assessing the Accuracy of Mosaics from Unmanned Aerial Vehicle (UAV) Imagery for Precision Agriculture Purposes in Wheat. Precis. Agric. 15, 44–56 (2014)

    Article  Google Scholar 

  5. Huerta, M.: Integration of civil unmanned aircraft systems (UAS) in the national airspace system (NAS) roadmap. Fed. Aviat. Adm. 19, 2013 (2013)

    Google Scholar 

  6. EASA, Policy for Unmanned Aerial Vehicle (UAV) Certification, EASA NPA 16/2005 (2005)

  7. CASA, AC 101-10 Remotely Piloted Aircraft Systems - Operation of Excluded RPA, Civil Aviation Safety Authority, Australia (2017)

  8. Stansbury, R.S., Vyas, M.A., Wilson, T.A.: A survey of UAS technologies for command, Control, and Communication (C3). In: Unmanned Aircraft Systems, pp. 61–78. Springer, Berlin (2008)

  9. ICAO: Manual on Remotely Piloted Aircraft Systems (RPAS). The International Civil Aviation Organization, Montreal (2015)

    Google Scholar 

  10. ICAO, 9613: Performance based Navigation (PBN) Manual, The International Civil Aviation Organization, Montreal, Canada (2008)

  11. Sabatini, R., Moore, T., Hill, C.: A new avionics-based GNSS integrity augmentation system: part 1–fundamentals. J. Navig. 66, 363–384 (2013)

    Article  Google Scholar 

  12. Sabatini, R., Moore, T., Hill, C.: A new avionics-based GNSS integrity augmentation system: part 2–integrity flags. J. Navig. 66, 501–522 (2013)

    Article  Google Scholar 

  13. Hausman, K., Weiss, S., Brockers, R., Matthies, L., Sukhatme, G.S.: Self-calibrating multi-sensor fusion with probabilistic measurement validation for seamless sensor switching on a UAV. In: 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 4289–4296 (2016)

  14. McGeer, T., Vagners, J.: Wide-scale use of long-range miniature AEROSONDEs over the world’s oceans. The Insitu Group, Bingen (1999)

    Google Scholar 

  15. Holland, G., Webster, P., Curry, J., Tyrell, G., Gauntlett, D., Brett, G., et al.: The AEROSONDE robotic aircraft: a new paradigm for environmental observations. Bullet. Amer. Meteorol. Soc. 82, 889–901 (2001)

    Article  Google Scholar 

  16. L. Unmanned Dynamics, Aerosim Blockset, Version 1.2 (2006)

  17. Burston, M., Sabatini, R., Clothier, R., Gardi, A., Ramasamy, S.: Reverse engineering of a fixed wing unmanned aircraft 6-DoF model for navigation and guidance applications. Appl. Mech. Mater. 629, 164–169 (2014)

    Article  Google Scholar 

  18. Julier, S.J., Uhlmann, J.K.: New extension of the kalman filter to nonlinear systems. In: AeroSense’97, pp. 182–193 (1997)

  19. Julier, S.J., Uhlmann, J.K.: Unscented filtering and nonlinear estimation. Proc. IEEE 92, 401–422 (2004)

    Article  Google Scholar 

  20. Lozano, J.G.C., Carrillo, L.R.G., Dzul, A., Lozano, R.: Spherical simplex sigma-point kalman filters: a comparison in the inertial navigation of a terrestrial vehicle. In: American Control Conference, 2008, pp. 3536–3541 (2008)

  21. Van Der Merwe, R.: Sigma-point kalman filters for probabilistic inference in dynamic state-space models. Oregon Health & Science University, Portlan (2004)

    Google Scholar 

  22. Allotta, B., Caiti, A., Costanzi, R., Fanelli, F., Fenucci, D., Meli, E., et al.: A new AUV navigation system exploiting unscented kalman filter. Ocean Eng. 113, 121–132 (2016)

    Article  Google Scholar 

  23. Yang, C., Shi, W., Chen, W.: Comparison of unscented and extended kalman filters with application in vehicle navigation. J. Navig. 70, 411–431 (2017)

    Article  Google Scholar 

  24. Tawk, Y., Tomé, P., Botteron, C., Stebler, Y., Farine, P.-A.: Implementation and performance of a GPS/INS tightly coupled assisted PLL architecture using MEMS inertial sensors. Sensors 14, 3768–3796 (2014)

    Article  Google Scholar 

  25. Cappello, F., Ramasamy, S., Sabatini, R.: A low-cost and high performance navigation system for small RPAS applications. Aerosp. Sci. Technol. 58, 529–545 (2016)

    Article  Google Scholar 

  26. Groves, P.D.: Principles of GNSS, inertial, and multisensor integrated navigation systems: Artech house (2013)

  27. Wan, E.A., Van Der Merwe, R.: The unscented kalman filter for nonlinear estimation. In: AS-SPCC. The IEEE Adaptive Systems for Signal Processing, Communications, and Control Symposium 2000, pp. 153–158 (2000)

  28. Ramasamy, S., Sabatini, R., Gardi, A.: A unified approach to separation assurance and collision avoidance for flight management systems. In: Proceedings of the 35th AIAA/IEEE Digital Avionics Systems Conference (DASC 2016), Sacramento, United States, 25-29 September 2016, pp. 1–8 (2016)

  29. Sabatini, R.: Cooperative and non-cooperative decision making for UAS detect-and-avoid: a novel unified approach. In: Emerging Disruptive Technologies Assessment Symposium 2015 Jointly organised by the Australian Defence Science and Technology Group and the University of New South Wales (2015)

  30. Lievens, K., Mulder, J., Chu, P.: Single GPS antenna attitude determination of a fixed wing aircraft aided with aircraft aerodynamics. In: AIAA Guidance, Navigation, and Control Conference and Exhibit, p. 6056 (2005)

  31. Bruggemann, T.S.: Investigation of MEMS Inertial Sensors and Aircraft Dynamic Models in Global Positioning System Integrity Monitoring for Approaches with Vertical Guidance. Queensland University of Technology, Brisbane (2009)

    Google Scholar 

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Authors and Affiliations

  1. School of Engineering – Aerospace and Aviation Discipline, Melbourne, VIC, 3000, Australia

    Francesco Cappello, Suraj Bijjahalli, Subramanian Ramasamy & Roberto Sabatini

Authors
  1. Francesco Cappello
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  2. Suraj Bijjahalli
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  3. Subramanian Ramasamy
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  4. Roberto Sabatini
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Correspondence to Roberto Sabatini.

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Cappello, F., Bijjahalli, S., Ramasamy, S. et al. Aircraft Dynamics Model Augmentation for RPAS Navigation and Guidance. J Intell Robot Syst 91, 709–723 (2018). https://doi.org/10.1007/s10846-017-0676-5

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  • DOI: https://doi.org/10.1007/s10846-017-0676-5

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