Research On Intelligent Decision Technology Of Weapon Station Based On Machine Learning

The change of people’s means of obtaining and using battlefield information promotes the transformation of war form.From the past mechanized war to today’s information war,and then to the coming intelligent war,every transformation process,no doubt due to the powerful push of information technology.Target threat assessment is one of the most important techniques of battlefield information utilization,which is the basis for commanders to make operational decision plans.It can also be said more bluntly that target threat assessment is the premise of firepower optimization of today’s army synthetic equipment system and the basis of autonomous decision-making of intelligent weapon platform in the future.Back propagation neural network algorithm is one of the most successful and widely used neural network algorithms.This method is widely used in various fields,especially in the prediction of target threat value,so it has been widely used in many scenarios.However,the results of the back propagation algorithm are also affected by the manual labeling results in the training samples,so its training results are controversial to some extent.For this algorithm,in order to obtain more accurate and reliable results,attention should be paid to the quality of samples and the accuracy of manual labeling.This paper explores the status of threat assessment in information fusion,summarizes the current situation of threat estimation research at home and abroad,analyzes the methods adopted by domestic and foreign scholars in target threat estimation research,introduces the steps of threat assessment in detail,and establishes the target threat assessment model according to the steps,and puts forward the target threat assessment evaluation index according to the characteristics of target threat assessment.Based on the traditional BP neural network algorithm,a high-dimensional BP neural network algorithm model is proposed in this paper.This method can effectively reduce the dependence on manual annotation.Then the relationship between the dimension of BP neural network and the accuracy of results is analyzed.