关键词: 辐射源识别, 瞬态特征, 特征融合, 概率神经网络

Abstract: Based on time-frequency analysis of the transmitters, a new transmitter individual identification method based on feature fusion is proposed. Firstly, the signal carrier frequency and transient amplitude characteristic are extracted, and then the fitting coefficients regarded as transient fingerprint characteristic are acquired by using segmented least squares curve fitting of the transient amplitude after resampling and cubic spline interpolation. Finally, the signal carrier frequency and the transient fingerprint characteristic are fused as the transmitter’s fingerprint feature vector as well as the fusion coefficients are optimized by genetic algorithms. Using probabilistic neural network classifier, the experiments are carried out based on 16 interphones. The experimental results show that the method is effectively, which is able to achieve available transmitter individual identification by mapping the signal time-frequency characteristics to the feature vectors, and the system recognition rate is above 90% with SNR of 20 dB.

Key words: transmitter identification, transient characteristics, feature fusion, probabilistic neural network