Threat Assessment of Aerial Target Based on Modified PSO Optimized BP Neural Network

Abstract

Aerial target threat assessment is to infer degree of target threat to us through comprehensive analysis of the target information, then sort on target, to provide the basis for commander of task allocation and tactical decision; it is the core of the air defense combat command and control system. Consider the aerial target threat assessment problem, A target threat assessment method is proposed by using Modified Particle Swarm Optimization (MPSO) algorithm to optimize the BP neural network. Firstly, this paper carries out analysis on the main factors affecting target threat assessment, then introduces the basic principle of BP neural network and particle swarm optimization (PSO) algorithm and its improvement, and the MPSO-BP neural network assessment model is constructed. Finally, the model is verified by simulation. Results show that the error of this method is obviously less than that of BP neural network and PSO-BP neural network assessment method and has a good evaluation effect.