A Bayesian Decision Model for Battle Damage Assessment.
AIR FORCE INST OF TECH WRIGHT-PATTERSONAFB OH
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Battle damage assessment BDA is critical to success in any air campaign. However, Desert Storm highlighted numerous deficiencies in the BDA process, and operations since Desert Storm continue to point out weaknesses. We present a review of the Phase I BDA decision, or physical damage assessment, and model the decision process using a Bayesian belief network. Through subject matter expert i.e., the targeteers elicitation sessions, imagery was found to be critically important to the BDA process yet this information is generally not retained. This use of perfect information is delineated in the BDA process models. We proposed a methodology based on Bayesian belief networks for incorporating this perfect information. We demonstrate the Bayesian belief networks capability to update conditional probability distributions using data generated in real world operations. This capability allows the networks conditional distributions to evolve, increasing model accuracy and reducing uncertainty in the decision.
- Operations Research
- Military Operations, Strategy and Tactics