Abstract
This paper describes a method to enhance current surveillance systems used in air traffic control. Those systems are currently based on statistical data fusion, relying on a set of statistical models and assumptions. The proposed method allows for the on-line calibration of those models and enhanced detection of non-ideal situations, increasing surveillance products integrity. It is based on the definition of a set of observables from the fusion process and a rule based expert system with the objective to change processing order, algorithms or even remove some sensor data from the processing chain.
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© 2011 Springer-Verlag Berlin Heidelberg
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Besada, J.A., Frontera, G., Bernardos, A.M., de Miguel, G. (2011). Adaptive Data Fusion for Air Traffic Control Surveillance. In: Corchado, E., Kurzyński, M., Woźniak, M. (eds) Hybrid Artificial Intelligent Systems. HAIS 2011. Lecture Notes in Computer Science(), vol 6679. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21222-2_15
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DOI: https://doi.org/10.1007/978-3-642-21222-2_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21221-5
Online ISBN: 978-3-642-21222-2
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