Multisensor integration in the tracking of landing aircraft

Author

Korona, Zbigniew ; Kokar, Mieczyslaw M.

Author_Institution

Dept. of Ind. Eng. & Inf. Syst., Northeastern Univ., Boston, MA, USA

fYear

1994

fDate

2-5 Oct 1994

Firstpage

771

Lastpage

778

Abstract

An algorithm is presented for tracking a landing aircraft using two different passive sensors, a laser range finder (LRF) and an infrared camera (FLIR). The main feature of this algorithm is its ability to identify and compensate for plume disturbance. The algorithm is based on the extended Kalman filter (EKF) and the filtering confidence function (FCF) which introduces a learning approach to the tracking problem. The results of a simulation using the learning tracking algorithm and the extended Kalman filter alone are presented and compared

Keywords

Kalman filters; aircraft; filtering theory; laser ranging; learning systems; optical tracking; sensor fusion; IR camera; extended Kalman filter; filtering confidence function; infrared camera; landing aircraft tracking; laser range finder; learning tracking algorithm; multisensor integration; passive sensors; plume disturbance; Aircraft propulsion; Cameras; Filtering; Filters; Industrial engineering; Information systems; Infrared sensors; Radar tracking; Sensor fusion; Target tracking;

fLanguage

English

Publisher

ieee

Conference_Titel

Multisensor Fusion and Integration for Intelligent Systems, 1994. IEEE International Conference on MFI '94.

Conference_Location

Las Vegas, NV

Print_ISBN

0-7803-2072-7

Type

conf

DOI

10.1109/MFI.1994.398377

Filename

398377