Combat Identification With Bayesian Networks
George Laskey, Washington Navy, Yard Dc, Kathryn Laskey · 2002
Correctly identifying tracks is a difficult but important capability for U.S. Navy ships and aircraft. It is difficult because of the inherent uncertainty, complexity, and short timelines involved. It is important because the price of failure is missed or civilian engagements and fratricide. Today, Navy ships and aircraft primarily use an If-Then rule-based system in evaluating radar and IFF information to perform Combat Identification (CID). To cope with the uncertainty and complexity of CID, Bayesian Networks have been suggested to integrate radar, IFF, and other lower quality sources to perform the identification determination. The goal of this project is to show that Bayesian Networks can be used to support CID investment decisions. Two investments, a new sensor and good maintenance, were compared in a difficult CID scenario in four different environments. The paper applies techniques from decision analysis and Bayesian networks to address the challenges of CID. The CID network was developed using good knowledge engineering practices.