An overview is given of a Bayesian tracking system for a multi-sensor environment. The main modules perform track initiation, track continuation and systematic error estimation, respectively. The track continuation module plays for Air Traffic Control the most important role. It consists of a combination of those approximate Bayesian methods that proved to be the most efficient for the main problems of track continuation: Extended-Kalman filtering for nonlinear dynamics, Probabilistic Data Association for unassociated measurements and Interacting-Multiple-Model filtering for sudden maneuvers. Comparisons of this new tracking system with alpha-beta Kalman based and state-of-the-art tracking systems show its superiority for application to Air Traffic Control surveillance. It provides better track continuity, more accurate expectations of position and velocity and more complete additional information in the form of probabilities of modes of flight (turning, accelerating and straight modes) and consistent estimates of its own accuracy. With this track information, advanced Air Traffic Control systems may better cope with the many uncertainties that are inherent to air traffic.
Bayesian Multi-Sensor Tracking for Advanced Air Traffic Control Systems
1990
8 pages
Report
Keine Angabe
Englisch
Air Transportation , Guidance Systems , Radiofrequency Detection , Air traffic control , Radar tracking , Bayes theorem , Error analysis , Radar networks , Tracking filters , Air traffic , Aircraft maneuvers , Kalman filters , Multisensor applications , Probability theory , Surveillance radar , Foreign technology
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