Radar emitter recognition method based on AdaBoost and decision tree
Xiaojing Tang, Weigao Chen, Weigang Zhu · 2017
For the poor real-time, robustness and low recognition accuracy of traditional radar emitter recognition algorithm in the current high density signal environment, this paper studied a kind of radar source recognition algorithm based on decision tree and AdaBoost.Firstly, the information gain can be used to construct single decision tree.Then using AdaBoost algorithm to train the weak classifier, and get a strong classifier.Finally, get the recognition results through the strong classifier.Simulation results show that the recognition accuracy of proposed method can reach 93.78% with 10% parameter error, and the time consumption is lower than 1.5s, which has a good recognition effect.