A Fusion and Learning Algorithm for Landing Aircraft Tracking: Compensating for Exhaust Plume Disturbance
نویسندگان
چکیده
|An algorithm is presented for tracking a landing aircraft using fusion of two diierent passive sensors, a laser range nder (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 lter (EKF) and the ltering conndence 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.
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