Individual Identification Method based on PACGAN and Differential Constellation Trace Figure

One of the outstanding problems of applying deep learning to solve Individual Identification is that it is difficult to collect enough samples to train the network. In order to solve this problem, an individual identification algorithm based on PACGAN is proposed. The algorithm processes the Differential Constellation Trace Figure of input signals, and improves the adaptability of ACGAN. This paper improves the adaptability of ACGAN, introduces pooling layer in the discriminator network to enhance its feature extraction ability in multi classification task; for the situation of a large number of edge distribution of sample image features, adds zero filling layer and increases convolution kernel receptive field to enhance its edge feature extraction ability. The results of five kinds of ZigBee devices show that the proposed algorithm has higher accuracy than other methods in the case of small sample set.