Unknown Radar Waveform Recognition Based on Transferred Deep Learning

Abstract

Radar signals are emerging constantly for urgent task because of its complex patterns and rich working modes. For some radar waveforms with known modulation methods, they can be identified by correlation between radar prior knowledge and the received signals by the reconnaissance receiver. As for the unknown radar signals, how to identify unknown radar waveforms under the condition of limited samples and low signal-to-noise ratio is a challenging problem. Aiming at the learning ability of the deep features of the image by the convolutional neural network (CNN), the reconstructed features of the time-frequency image (TFI) of the known and unknown radar waveform signals have been excavated. A decision fusion unknown radar signal identification model based on transfer deep learning and linear weight decision fusion is designed in this paper. Firstly, the CNN is trained using the known radar signals; Then, based on the transfer learning, the neurons obtained from the multiple underlying the CNN are used to represent the reconstruction feature; Finally, the performance of the single random forest classifier of the original TFI and short- time autocorrelation features images (SAFI)are fused, the identification decision of unknown signals is realized by setting linear weight to the two databases. The recognition rate of unknown new classes for small samples exceeds 80.31%, and the classification accuracy rate for known radar waveform reach more than 99.15%.