Radar Seeker Anti-jamming Performance Prediction and Evaluation Method Based on the Improved Grey Wolf Optimizer Algorithm and Support Vector Machine

摘要

In order to accurately evaluate the anti-jamming performance of radar seeker, an Improved Grey Wolf Optimizer (IGWO) algorithm is proposed to optimize the estimation and prediction of Support Vector Machine (SVM) parameters. Firstly, according to the characteristics of radar seeker, this paper constructs the index system of anti-jamming performance of radar seeker, and then, this paper introduces chaotic search mechanism, convergence and nonlinear adaptive weight to improve the traditional Grey Wolf Optimizer algorithm for a better global optimization ability. Finally, by using the IGWO algorithm to optimize the related parameters of Support Vector Machines (SVM), a comprehensive evaluation method is proposed for simulation experiment. Simulation results show that the proposed method has higher prediction accuracy and better generalization ability than SVM model and BP neural network.