Situation assessment in the warships-airplanes joint operation based on parameter learning in Bayesian network
Changliang Xu, Yuhui Wang, Wenlei Ai · 2014
This paper presents a situation assessment system structure of the warships-airplanes joint operation and a new situation assessment algorithm based on parameter learning in Bayesian network. Previously, situation assessments are usually operated by using a normal kind of Bayesian network with immutable probability distribution, which may cause the evaluation is too subjective. Therefore, a Bayesian assessment network with parameter learning is considered for a more objective result of the situation assessment. Then a new assessment algorithm of the parameter learning is discussed, which will improve the performance of the assessment. Finally, a model of the battlefield situation assessment of the warships-airplanes joint operation is presented. Its situation assessment simulation results verify the effectiveness of the proposed algorithm.