A cloud Bayesian network approach to situation assessment of scouting underwater targets with fixed‐wing patrol aircraft

Abstract

AbstractThe battlefield situation changes rapidly because underwater targets' are concealment and the sea environment is uncertain. So, a great number of situation information greatly increase, which need to be dealt with in the course of scouting underwater targets. Situation assessment in sea battlefield with a lot of uncertain information is studied, and a new situation assessment method of scouting underwater targets with fixed‐wing patrol aircraft is proposed based on the cloud Bayesian network, which overcomes the deficiency of the single cloud model in reasoning ability and the defect of Bayesian network in knowledge representation. Moreover, in the method, the cloud model knowledge deal with the input data of Bayesian network reasoning, and the advantages in knowledge representation of cloud theory and reasoning of Bayesian network are applied; also, the fuzziness and stochasticity of cloud theory in knowledge expression, the reasoning ability of Bayesian network, are combined. Then, the situation assessment model of scouting underwater targets with fixed‐wing patrol aircraft is established. Hence, the directed acyclic graph of Bayesian network structure is constructed and the assessment index is determined. Next, the cloud model is used to deal with Bayesian network, and the discrete Bayesian network is obtained. Moreover, after CPT of each node and the transformation between certainty degree and probability are accomplished; the final situation level is obtained through a probability synthesis formula. Therefore, the target type and the operational intention of the other side are deduced to form the battlefield situation. Finally, simulations are carried out, and the rationality and validity of the proposed method are testified by simulation results. By this method, the battlefield situation can be gained. And this method has a wider application range, especially for large sample data processing, and it has better practicability.