Target Threat Assessment in Air Combat with BP Neural Network for UAV

Abstract

Target threat assessment is the most important element in command control communication and computer intelligence surveillance reconnaissance (C4ISR) system which is the key function in air combat situation analysis and can select the most valuable target to the commander. A threat assessment model of multi-UAV platform cooperative engagement built based on back propagate neural network (BPNN) is present in this paper, which provides a machine learning method for the threat assessment. The BPNN target threat assessment model has six input nodes, 10 hide nodes and 1 output node, which can fit the complicated nonlinear relationship between threat value and target behavior state. Simulation experiment is executed to verify the accuracy of the proposed BPNN target threat assessment method, and the result shows that the method is effective and can be applied to the real air combat.