Multifunctional Radar Jamming Effect Evaluation based on Optimized Limit Machine Learning

Taking multifunctional radar as the object of interference research, according to the radar working characteristics, the signal return rate and signal parameters are analyzed in the radar working state change, and the radar takes anti-jamming measures to change. According to the signal return rate and radar signal parameters to establish a sample library, the algorithm based on genetic algorithm (GA) optimization of the extreme learning machine (ELM) is proposed for the evaluation of the interference effect, and compared with the unoptimized ELM algorithm. The average accuracy of the algorithm before and after optimization is 78.75% and 96%, respectively.