Abstract: Aiming at the problem of unreasonable virtual force tactical confrontation action parameters in the simulation training of armored force equipment system, resulting in unsatisfactory training effects, the idea of estimating posterior probability with prior probability is adopted to correct the parameters. According to the OODA theory, the tactical confrontation movement of armored forces is decomposed and an index system of influencing factors is constructed; the parameter collection method is proposed to establish the Bayesian network correction model and to realize dynamic adjustment; the expected optimization algorithm and the principal component analysis method are used to process the training data of the simulator to verify the effectiveness of the method. The comparison between the experimental results and the actual data shows that the method has a high confidence.

Key words: virtual force, tactical action, dynamic Bayesian networks, expectation-maximization algorithm