Adjust fire goal with UAV based on dynamic bayesian network
Qinkun Xiao, Xiaoguang Gao · 2005
Dynamic Bayesian Networks are a powerful methodology for representing and computing uncertain problem of stochastic processes.The changing situation complicates information disposal and impacts on direction and manner of decision-making.The paper brings out that combine Hidden Markov Models with Fuzzy inference to come into being Dynamic Bayesian Networks and use the graph to analyse spy information that gained from war field.Firstly,we can build a battlefield dynamic model based on Dynamic Bayesian Networks.Secondly,we use a Viterbi algorithm to inference and get the best estimate about hidden sequence.At the same time,we can predict trend of changing war field for the future.The next step,we can use Fuzzy inference to get the best decision or to apply decision-making first.In the end,we do an emulational a examination to prove the idea is right.