Radar jamming effect evaluation based on AdaBoost combined classification model
Qin Futong, Meng Jie, Jing Du, Fujiang Ao, Zhou Ying · 2013
The radar jamming effect evaluation can be solved by translating to multi-class classification problems. In this paper, we propose using the AdaBoost combined classification model to evaluate radar jamming effect. An evaluation model is designed, which uses Support Vector Machine as component classifiers and the AdaBoost M1 algorithm as combined classifier. The experiments show that, the model designed in this paper can be used to evaluate radar jamming effect, and its evaluation accuracy is much higher than some single classifiers, such as RBF neural network and Bayes.