Abstract
Radar emitter identification based on machine learning technology at present mostly assumes that the test set is identically distributed with and the training set, which causes the classification effect is not well when the database samples and the true distribution of the signals are biased. Thus, the theory of transfer learning is introduced into the identification system, and a radar emitter signal identification method based on structural discovery and re-balancing is proposed. By means of database data and target data clustering analysis and re-sampling, correct the distribution and put the new data to the Support Vector Machine (SVM) for training and identifying reconnaissance samples. The simulation results show that the classification performance of the Support Vector Machine model in the new training sample set has been greatly improved.
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