Measurement and evaluation method of radar anti-jamming effectiveness based on principal component analysis and machine learning

Abstract

AbstractWith the development of modern electronic countermeasure technology, the fight between radar jamming and anti-jamming has become increasingly fierce. Experts have done a lot of highly effective work on radar anti-jamming performance. However, the emergence of various new complex interferences has rendered existing methods unable to meet the needs. In this manuscript, we consider the measurement and evaluation method of radar anti-jamming effectiveness based on principal component analysis and machine learning. Firstly, taking into account the diversity of variables in radar countermeasure experiments and the complexity of constraints between variables, we propose a bipartite covering array for the experimental scheme, which requires that each level combination of any radar parameter and jammer parameter occurs at least once, to ensure the rationality of the experiments. Secondly, according to the characteristics of multiple jammers and the analysis of impacts on radar performances, we combine the existing indicators and use the principal component analysis method to obtain two comprehensive indicators, which better reflect radar performances. Finally, we select the best model as a prediction for radar comprehensive indicators by comparing several machine learning algorithm models, including classification and regression tree, random forest, xgboost, and SVM. Additional experiments verify the effectiveness of the resulted model.