Fixed-wing unmanned aerial vehicle situation prediction method based on Bayesian neural network

The invention provides a fixed-wing unmanned aerial vehicle situation prediction method based on a Bayesian neural network, and belongs to the technical field of unmanned aerial vehicle situation prediction. The technical problem that the our unmanned aerial vehicle cannot perform uncertainty prediction on the future situation of the enemy unmanned aerial vehicle in an uncertain environment is solved. According to the technical scheme, a Bayesian network suitable for time sequence prediction is established, and finite situation information of an enemy unmanned aerial vehicle is collected; taking the situation information of the enemy unmanned aerial vehicle as input, and predicting the situation of the enemy unmanned aerial vehicle at the next moment by using the established Bayesian neural network; and predicting again by taking the single moment predicted value as input to form situation information of the enemy unmanned aerial vehicle in a future time period. The method has the beneficial effects that the