Abstract:

Aiming at the problems that most existing machine learning-based classification of radar active jamming need to construct artificial feature sets and low classification accuracy in the case of small samples, an ensemble convolutional neural network (CNN) classification method based on multichannel feature fusion is proposed. Firstly, multiple mathematical models of active jamming are established, simulated and the corresponding time-frequency profiles are obtained by using the short-time Fourier transform (STFT). Secondly, real part, imaginary part, and modulus three-channel features of time-frequency distribution plots are extracted to establish sample sets containing different combinations of features through multiple feature combinations. Ultimately, an ensemble depth model with CNN as the base classifier is constructed, each CNN separately extracts the features of different sample sets, and majority voting on the prediction results of all base classifiers gives the overall prediction results of the ensemble model. The experiments show that the proposed method can effectively realize highly accurate intelligent identification of multiple classes of active jamming in the case of small samples.

Key words: active jamming classification, short-time Fourier transform (STFT), ensemble learning, convolutional neural network (CNN), small sample