On The Identification Technique Of Individual Transmitter Based On Signalprints

As a very important subject in the filed of military communications confrontation, in-dividual transmitter identification technique is able to identify individual equipment using the subtle features of individual transmitted signal resulting from differences in hardware, and then the transmitter tracking, targeted surveillance, electronic jamming or military at-tack to the enemy's electronic equipment and their carriers can be enacted. Different from modulation mode identification, individual transmitter identification mainly researches the extraction of individual subtle features between transmitters with the same model.Currently, the transient feature extraction using the boot signal is mainly researched for individual transmitter identification. However, transmitter identification based on tran-sient features faces some challenges, such as transient signal capture in non-cooperative communication environment and difficulties in feature extraction caused by similarity between transient signal and noise. Also, most methods work in higher signal-to-noise with sufficient samples, and mainly solve the problem of identifying transmitters with dif-ferent models. Actually, the received signals often have low SNR, and the seized signals have short duration, which results in insufficient samples. Thus, the recognition rate is low when using the existing methods. Therefore, this paper mainly researches the extraction technique of individual subtle features(signalprints) using the steady signal and aims at the problem of identifying individual transmitter with the same model.The basic theory of signalprints is researched. Based on signalprints mechanism, the characteristics of frequency, modulation parameters and stray output resulting from indi-vidual transmitter are researched, and methods for extracting signalprints are explored from different angles, such as time domain, frequency domain and high order spectra, etc. After that, an architecture of individual transmitter identification is established. The pro-posed algorithms are verified to be efficient using the measured radio data.The main areas in this paper are as follows.(1)The signalprints extraction based on carrier frequency and symbol rate is studied. An improved method of phase fitting is proposed to estimate the carrier frequency, and a method based on STFT time-frequency energy distribution and wavelet analysis is given for estimating the symbol rate of modulation signal in non-cooperative communication environment. Experimental results show that estimated frequency and symbol rate can be used as part of signalprints, and cooperate with other features for identifying individual transmitter.(2)The stray output of individual signal is used to extract signalprints. First, a method of orthogonal component reconstruction is presented for extracting signal envelop, and the fractal dimensions and Lempel-Ziv complexity are used to extract the spurious modulation features of signal envelop. Then the individual differences of Hilbert edge spectrum sym-metry parameter and Hilbert Huang Transform time-frequency distribution(HHT-TFD) grayed image are studied for classifying. Experiments show that complexity features of envelop and HHT-TFD grayed image have better separability in lower SNR, whereas spectrum symmetry features perform worse as affected by the estimation accuracy of car-rier frequency.(3)The high order spectrum is used to extract signalprints. The square integrated bis-pectra(SIB) are proposed, and an improved local linear embedded method is used to re-duce dimension of SIB features. Studies show that SIB features are superior to the other local bispectra when extracting signalprints, and the low-dimensional SIB features have good performance of clustering and noise suppresion.(4)Aimed at actual applications of transmitter identication, a multi-class SVM classi-fier based on kernel distance measurement and a combination classifier based on D-S evi-dence theory are proposed for verifying the effectiveness of signalprints extracted from different angles. Experiments show that the feature set of signalprints extracted from steady signal can achieve high recognition rate for measured radio data.This paper solves the problem of identifying individual transmitter with the same model under the conditions of small samples, lower and variant SNR to some extent. The research has some theoretical and practical application prospects.