Radar Signal Recognition Method Based on Random Forest Model

As a key step of electronic reconnaissance, radar signal recognition plays a decisive role in the deployment and control of electronic defense, electromagnetic interference attack and the formulation of operational plan. It is of great significance and research value for electromagnetic warfare and national defense security under modern information warfare. In order to identify the Platform Type of radar through various parameters of radar signal in the complex electromagnetic environment, this paper comprehensively analyzes the characteristics of radar signal and the development and evolution of relevant identification methods. Combined with the excellent performance of machine learning in classification and prediction, this paper deeply studies the process of model establishment, evaluation and optimization for Random Forest classification algorithm, and completes the process from algorithm programming to training and generating model to experimental test and verification. The experiment shows that after optimization, the prediction accuracy reaches 0.979, and the F1-score reaches 0.980, which provides an effective support for improving the efficiency of radar reconnaissance and early warning.