Abstract
Combat survivability is an important objective in military air operations, which involves not being shot down by e.g. enemy aircraft. This involves analyzing data and information, detecting and estimating threats, and implementing actions to counteract threats. Beyond visual range missiles can today be fired from one hundred kilometers away. At such distances, missiles are difficult to detect and track. The use of techniques for recognizing hostile aircraft behaviors can possibly be used to infer the presence and for providing early warnings of such threats. In this paper we compare the use of dynamic Bayesian networks and fuzzy logic for detecting hostile aircraft behaviors.
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DSL: Genie and smile. Decision Systems Laboratory, University of Pittsburgh, Pittsburgh, PA, USA
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Dahlbom, A. (2013). A Comparison of Two Approaches for Situation Detection in an Air-to-Air Combat Scenario. In: Torra, V., Narukawa, Y., Navarro-Arribas, G., Megías, D. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2013. Lecture Notes in Computer Science(), vol 8234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41550-0_7
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DOI: https://doi.org/10.1007/978-3-642-41550-0_7
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