The air traffic control (ATC) system is critical in maintaining the safety and integrity of the National Airspace System (NAS). This requires the information fusion from various sources. This paper introduces a hybrid network model called the Bayesian-Entropy Network (BEN) that can handle various types of information. The BEN method is a combination of the Bayesian method and the Maximum Entropy method. The Maximum Entropy method introduces constraints and is given as an exponential term added to the classical Bayes’ theorem. The exponential term can be used to encode extra information in the form of constraints. The extra information can come from human experience, historical data etc. These knowledges, once written in a mathematical format, can be incorporated into the classical Bayesian framework. The BEN method provides an alternative way to consider common data types (e.g., point observation) and uncommon data types (e.g., linguistic description for human factors) in the NAS. The reported work is demonstrated in two example problems. The first example involves an air traffic control network model and the BEN uses information from various sources to update for the risk event probability. The second example is related to the prediction of the cause of runway incursion. A network model studying different sources of error is used to make predictions of the cause of runway incursion. The training and validation data is extracted from existing accident report in the Aviation Safety Reporting System (ASRS) database. The results are compared with that of the traditional Bayesian method. It is found that the BEN can make use of the available information to modify the distribution function of the parameter of concern.
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Bayesian network, Maximum entropy, probability, inference
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