A novel dynamic Bayesian network based threat assessment algorithm
Zhenhua Fan, Benhui Shi, Jinyong Chen, Tongle Duan · 2017
The traditional threat assessment (TA) methods are confronted with the problems that most of them only focus on the static threat of a single target and the threshold of threat degree fusion is hard to set. For this reason, a novel DBN (dynamic Bayesian network) based TA algorithm is proposed. In the proposed algorithm, firstly, DBN is constructed with various factors, i.e., terrain, weather, time, relative strength, distance and velocity vector, for the TA of group targets. Then, the fast approximate inference is implemented according to Markov property. Finally, the probabilities of threat degrees are integrated into the continuous threat index and the discrete threat degree. Simulation results show that the proposed algorithm can be used to reliably and dynamically evaluate the threat of group targets in complex environment.