Evaluation of Intelligent Air Defense algorithm based on Machine Learning

Abstract

In the complex and rapidly changing combat environment, the enemy interference and sensor performance limitations and other factors lead to insufficient battlefield information. In order to make the UAV have the ability to carry out threat assessment under the condition of insufficient information, a Bayesian network (BN) threat assessment modeling method based on small data sets is proposed in this paper. Starting with the two problems of BN structure learning and parameter learning under the condition of small data set, using the constraint matrix obtained by Bootstrap method as the constraint item added to the score function, a BN structure learning algorithm based on small data set is proposed, and the BN parameter learning algorithm based on interval prior constraint is adopted. The simulation results show that, compared with the traditional BN learning algorithm, the BN learning algorithm proposed in this paper has higher accuracy and availability in UAV threat assessment modeling under the condition of small data set.