A fusion and learning algorithm for landing aircraft tracking: compensating for exhaust plume disturbance

Author

Kokar, Mieczyslaw M.

Volume

31

Issue

3

fYear

1995

fDate

7/1/1995 12:00:00 AM

Firstpage

1210

Lastpage

1215

Abstract

An algorithm is presented for tracking a landing aircraft using fusion of two different passive sensors, a laser range finder (LRF) and a forward-looking infrared (FLIR) camera. The main feature of this algorithm is its ability to identify and compensate for an exhaust 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 EKF alone are presented and compared

Keywords

Kalman filters; aerospace computing; aircraft control; aircraft landing guidance; compensation; digital simulation; infrared detectors; laser ranging; learning (artificial intelligence); sensor fusion; target tracking; different passive sensors; exhaust plume disturbance; extended Kalman filter; filtering confidence function; forward-looking infrared camera; landing aircraft tracking; laser range finder; learning algorithm; learning tracking algorithm; sensor fusion; tracking; Azimuth; Cameras; Filtering; Goniometers; Infrared sensors; Laser fusion; Military aircraft; Radar tracking; Sensor fusion; Sensor systems;

fLanguage

English

Journal_Title

Aerospace and Electronic Systems, IEEE Transactions on

Publisher

ieee

ISSN

0018-9251

Type

jour

DOI

10.1109/7.395215

Filename

395215