Abstract: When extracting the subtle features of interference signals, the traditional identification methods of interference source signal types have some shortcomings, such as low accuracy and poor recognition effect. In this paper, a deep neural network based ADS-B interference signal modulation type recognition algorithm is proposed. Firstly, ADS-B signal and interference waveform are superimposed and mixed. Simulation signals are transmitted by controlling vector signal generator (VSG) and collected at the receiving end. Then, random noise is artificially added to the received baseband I and Q data, and based on this, tensor training sample datasets are constructed under various SNR scenarios. Finally, the training sample data are used to train the neural network designed in this paper, and the recognition performance of the traditional classification algorithm and that of the neural network algorithm proposed in this paper are compared and analyzed in the sample data set. Experimental results show that the neural network algorithm proposed in this paper has better recognition performance compared with the existing traditional recognition algorithms.

Key words: deep learning, ADS-B, identification of modulation type, convolutional neural network, residual neural network