A Multi-Modal Unscented Kalman Filter for Inference of Aircraft Position and Taxi Mode from Surface Surveillance Data
Name
Balakrishnan_A Multimodal.pdf
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1.18 MB
Format
Adobe PDF
Checksum (MD5)
2889b4e053ea5c72f2365d7076fd4eb2
Author(s)
Balakrishnan, Hamsa
Khadilkar, Harshad Dilip
Date Issued
September 2011
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
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.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Massachusetts Institute of Technology. Engineering Systems Division
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike 3.0
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.2514/6.2011-7051
