关键词: 个体识别, 频率稳定度, 瞬时相位, 信息维数, 抗加性高斯白噪声

Abstract: Under the condition of low signal-noise-ratio, the individual features of communication transmitter in steady-state signal are covered very easily, and hard to be extracted and identified. In regard to this problem, considering the fact that oscillators used in different communication transmitters have unequal frequency stability, a new method based on fractal dimension and Support Vector Machine(SVM) was proposed. After oversampling IF(Intermediate Frequency) signal, information dimension was extracted as features, and then SVM classifier was designed to realize the automatic identification of unknown samples. To some extent, the feature was robust under AWGN (Additive White Gaussian Noise). The computer simulation shows that the method has good performance on classifying five PSK signals with the same order and the frequency stability difference of 0.01ppm under 3dB, its accuracy being 95%.

Key words: individual identification, frequency stability, instant phase, fractal dimension, anti-AWGN