Situation Assessment Model for UAV Disaster Relief in the City

A situation assessment model based on structurevariable discrete dynamic Bayesian network (SVDDBN) of sorting information is proposed for Unmanned Aerial Vehicle (UAV), which can be applied in the condition of disaster relief in the city when pop-up threats appear. The model is built on the basis of the SVDDBN, makes an uncertain classification on pop-up threats observing information with the help of the posterior probability support vector machine (PPSVM), and finally inputs the classification results into the assessment model as the evidence. For the features of the multi-hidden nodes of the assessment model, the forward algorithm is introduced into the probability inference of the network model, and the inference algorithm of the SVDDBN under the multi-nodes is worked out. The situation that the UAV detects the pop-up threats in the air while conducting the disaster relief in the city is set as the background to verify the correctness of the establishment of the model and the related algorithm.