Abstract:

In order to effectively solve the problem of specific emitter identification, a method based on the combination of Hilbert-Huang transform and adversarial training is proposed. Firstly, a mathematical model of the emitter signal according to the hardware difference of the emitter is established. Secondly, the Hilbert-Huang transform is performed on the signal to obtain the Hilbert spectrum. Then, in the preprocessing process, from the energy values corresponding to all Hilbert spectrum time-frequency points of the the signal, the most distinguishable set of energy values is determined, and the corresponding time-frequency points are recorded. Finally, the energy values corresponding to the time-frequency points recorded above for the Hilbert spectrum of each type of emitter signal is extracted. And then, it is sent to the convolutional neural network for training and testing, and the anti-noise performance of the network is improved by means of adversarial training. The identification accuracy experiment shows that comparing the method without adversarial training and the method without preprocessing and adversarial training, the identification accuracy of the proposed algorithm is increased by 3.1% and 5.5%, respectively. The identification robustness experiment shows that the proposed algorithm can achieve great identification performance when the training sample is 100, and the advantages become more obvious as the number of radiation sources emitter increases. The complexity analysis shows that the proposed algorithm can effectively reduce the amount of calculations generated by the neural network during a large number of training and recognition processes.

Key words: specific emitter identification, Hilbert-Huang transform, convolutional neural network, adversarial training