A Multi-Modal Unscented Kalman Filter for Inference of Aircraft Position and Taxi Mode from Surface Surveillance Data

Author(s)
Balakrishnan, Hamsa; Khadilkar, Harshad Dilip
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Abstract
We describe a multi-modal unscented Kalman lter developed for estimation of aircraft position, velocity and heading from noisy surface surveillance data. The raw data is composed of tracks generated by the Airport Surface Detection Equipment, Model-X at Boston Logan International Airport, and is obtained from the Runway Status Lights system. The multi-modal lter formulation facilitates estimation of aircraft taxi mode, described by di erent acceleration and turn rate values, in addition to aircraft states.
Date issued
2011-09
URI
http://hdl.handle.net/1721.1/80334
Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics; Massachusetts Institute of Technology. Engineering Systems Division
Journal
Proceedings of the 11th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference
Publisher
American Institute of Aeronautics and Astronautics
Citation
Khadilkar, Harshad, and Hamsa Balakrishnan. “A Multi-Modal Unscented Kalman Filter for Inference of Aircraft Position and Taxi Mode from Surface Surveillance Data.” American Institute of Aeronautics and Astronautics, 2011.
Version: Author's final manuscript
ISBN
978-1-60086-941-9
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