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Introduction to the

It can be learned from the previous two articles that Bayesian networks are mainly divided into static Bayes and dynamic Bayes. In today’s society, the application of dynamic Bayes has been quite extensive compared with that of static Bayes. I saw a journal two days ago and felt inspired. I share it with you here. I hope it can be helpful to you.

Dynamic bayes

In the unmanned vehicles based on dynamic bayesian networks target threat assessment, because no one chariot target threat assessment is one of the more complex problems, the target threat degree is mainly composed of combat mission and target characteristics decided target, in the process of fighting, according to the target information provided by the sensor, the target threat assessment on the basis of situational awareness. The nature of threat assessment is uncertain decision making. Traditional threat assessment methods are difficult to ensure the correctness of decision results because of insufficient use of battlefield situation information.

Because dynamic Bayesian network can overcome the subjectivity and uncertainty brought by static evaluation, it is more suitable for unmanned vehicle combat environment with high dynamic and strong confrontation. Therefore, dynamic Bayesian networks are more suitable than static Bayesian networks.

Firstly, target features are selected and fuzzy processing is carried out. Secondly, an inference model for threat assessment is established based on static Bayesian network (SBN). Then, the inference model is extended in time dimension according to dynamic Bayesian network (DBN) theory, and the dynamic inference model is established. The simulation results are compared with SBN inference results.

remarks

This paper first briefly introduces the content of dynamic Bayes and how dynamic Bayes is applied in real life. The content is not very much, the specific content will be presented to you after sorting out later.