This paper considers the situational awareness function associated with an Unmanned Aerial Vehicle (UAV) arriving at an uncontrolled airfield. Given no air traffic control service available within such a terminal area, the UAV needs to establish a good level of situation awareness by using its onboard sensors to detect and track other traffic aircraft. Comparing to existing works which mainly use sensor observations in the filtering process,
this paper exploits the circuit flight rules to provide extra knowledge about the target
behaviour. This is achieved by using the multiple models to describe the target motions in different flight phases and characterising the phase transition in a stochastic manner. Consequently, an interacting multiple model particle filter with state-dependent transition probabilities is developed to provide the required situation awareness function.
Funding
This work was supported by the U.K. Engineering and Physical Sciences Research Council (EPSRC) Autonomous and Intelligent Systems programme under the grant number EP/J011525/1 with BAE Systems as the leading industrial partner.
History
School
Aeronautical, Automotive, Chemical and Materials Engineering
Department
Aeronautical and Automotive Engineering
Published in
Proceedings of the IMechE, Part G: Journal of Aerospace Engineering
Citation
LIU, C. ...et al., 2016. Enhanced situational awareness for unmanned aerial vehicle operating in terminal areas with circuit flight rules. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 230 (9), pp.1683-1693.
This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/
Publication date
2016
Notes
This paper was accepted for publication in the journal Proceedings of the IMechE, Part G: Journal of Aerospace Engineering and the definitive published version is available at: https://dx.doi.org/10.1177/0954410016636156