Machine learning-based electronic confrontation system performance evaluation and optimization

Electronic confrontation is a key technology in modern warfare, and its complexity and uncertainty brought challenges to system performance evaluation and optimization. This article studies the performance evaluation and optimization method of electronic confrontation system based on machine learning. In terms of performance evaluation, a data-driven assessment framework is proposed. The construction of electronic confrontation efficiency is realized through feature engineering and machine learning model construction. Experimental results show, This method can accurately evaluate the combat effectiveness of electronic confrontation equipment and strategies in complex electromagnetic environments, where the performance of neural network models is the best. In terms of performance optimization, the electronic confrontation system parameter optimization method based on Bayesian optimization and electronic confrontation strategy optimization model based on deep reinforcement learning. The study of this article enriches the theoretical and methods of machine learning in the field of electronic confrontation, and provides new ideas for the intelligent development of the electronic confrontation system.