Radar emitter signal recognition based on convolutional neural network and main ridge coordinate transformation of ambiguity function
Yunwei Pu, Can Liu, Haixiao Wu, Jiang Hong Guo · 2021
Aiming at the time-consuming and inconspicuous features of artificially extracting radar emitter signal features, a recognition method based on deep learning convolutional neural network and ambiguity function main ridge coordinate transformation is proposed. The method extracts the main ridge of the ambiguity function of the signal through fast discrete fractional Fourier transform, and then uses the two-dimensional time-frequency diagram of the main ridge polar coordinate domain of the ambiguity function as the input of the convolutional neural network to realize the sorting and recognition of different radar signals. Experiments show that the method in this paper not only maintains a 100% recognition rate above 0dB, but also stabilizes the recognition accuracy rate above 90% at -6dB. Compared with traditional radar signal recognition methods and other deep learning model recognition methods, the proposed method has greatly improved recognition rate and robustness, and has certain engineering application value.