Abstract: A new damage assessment method based on BN-Cloud model is put forward for the impact of complex and uncertain factors in modern battlefield environment. The characteristics of targets are analyzed, and a hierarchical evaluation index system is established, which is used to build a Bayesian network (BN) structure. Monte Carlo method is introduced into parameter learning, a condition probability table (CPT) of each network node is obtained through simulation, and the probability of each damage level is obtained by using the network inference. Finally, cloud model is used to transform target damage probability into real damage value so as to realize the transformation from uncertainty to certainty. The target physical damage degree is used as one of sub-nodes of functional damage degree in BN, in which CPT is used as a link. As a result, a new method to transform physical damage into function damage is realized. A radar target is taken as an example for simulation. The simulated result shows that the proposed method can be effectively used for target damage assessment, and achieves a significant improvement in accuracy and reliability compared with the existing algorithms.

Key words: ordnance science and technology, damage assessment, Bayesian network, cloud model, radar, Monte Carlo algorithm