Radar emitter recognition based on the short time fourier transform and convolutional neural networks
Xuebao Wang, Gaoming Huang, Zhiwen Zhou, Jun Yao Gao · 2017
To improve the recognition rate of radar emitters with complex signal system in an awful electromagnetic environment, a new recognition method based on short time Fourier transform (STFT) and convolutional neural networks (CNN) was proposed. In this method, STFT obtains the time-frequency distribution of radar emitter in-pulse modulated signals and CNN extracted the features of different radar signals with the processed data. Before the time-frequency distribution (TFD) arrays were input to the CNN, a base noise reduction was conducted after six-time zero-means scaling. In the end of the classification, an additional operation was made to distinguish NS and BPSK. Simulations were implemented to present the high recognition performance of the method.