A novel DBN-based intention inference algorithm for warship air combat

It is difficult to determine the dynamic Bayesian network (DBN) parameters applied to the inference of intention in the warship air defense environment. This paper considers time variables based on QMAP algorithm that uses both domain knowledge and a small number of static samples for parameter learning, and then we propose a D-QMAP algorithm suitable for DBN parameter learning, taking into account time variables, which utilizes the timing relationship between samples. The simulation is carried out on the background of warship air defense, and the results are in line with reality, verifying the rationality and effectiveness of the model and algorithm.