Threat Assessment of Air Cluster Targets Based on Dynamic Bayesian Network with Cloud Model

Traditional threat assessment methods, such as Analytic Hierarchy Process(AHP), are usually limited to air individual targets without considering air cluster targets. Moreover, due to the uncertainty and complexity of the battlefield environment, the data used for threat assessment is incomplete in many situations, such as interval values and probability values. Traditional methods cannot deal with incomplete information. Aiming at these problems, a cluster target threat assessment method based on dynamic Bayesian network (DBN) with cloud model is proposed in this paper. Based on the characteristics of air cluster targets, the DBN is constructed to infer the threat value of multiple air cluster targets with the Technique for Order Preference by Similarity to an Ideal Solution method (TOPSIS). On this basis, the cloud model with the ability to express uncertainty is used to deal with incomplete information. The experiments show that the proposed method can conduct a threat assessment of air cluster targets with incomplete information, and is more accurate compared to traditional threat assessment methods.