Radio Fingerprint Features Extraction Of Communication Signals And Specific Emitter Identification

Radio fingerprint features extraction of communication signals and specific emitter identification(SEI)are new research fields,which play an important role in communication confrontation and electronic reconnaissance,and are of important strategic significance.Although the fingerprinting mechanism of communication individuals has not been fully proved by theory,the fingerprint features extraction based on steady-state characteristics of unintentional modulation individual signals has been developed.The fingerprint mechanism relies on the nonlinear features of emitter power amplifier.It takes the signal as the carrier,and satisfies the uniqueness,independence and stability of the fingerprint.And the testability is also the focus of the fingerprint features extraction and SEI.In this paper,based on the fingerprint mechanism of the nonlinear power amplifier,the individual emitter model is built.Based on the steady-state characteristics of the communication signals,the fingerprints are extracted from the non-cooperative signals which are the same format and have the same parameters.With the fingerprint,the individual recognition system are built.The main research works are as follows:(1)The research background,purpose and significance of fingerprint features extraction and SEI are analyzed.And research status of fingerprint features extraction domestic and abroad are analyzed from transient characteristics and steady-state characteristics.(2)Analyze the fingerprinting mechanism of the communication emitter and model the behavior of the individual.It is considered that the fingerprint mechanism relies on the non-linear characteristic of emitter power amplifier,then the nonlinear distortion and memory effect of the power amplifier are analyzed theoretically.Taylor model and MP model are established.Different individuals can be got through seting differnet model parameters.And the AM/AM and AM/PM characteristics were analyzed.(3)The fingerprint features extraction and SEI based on HHT spectrum are studied.HHT-EC,HHT-CD and HHT-Fisher fingerprint features are analyzed respectively.With SVM classifier,different individual models(based on Taylor model or MP model,QPSK or QAM)were used to carry out the simulation experiments.The effectiveness of the three fingerprint algorithms are verified in SEI.(4)A fingerprint feature extraction algorithm and an individual recognition system based on approximate entropy(ApEn)are proposed.Aiming at the different nonlinear characteristics of individual signals,ApEn and EMD tools are used to measure the non-linearity of the signals from multi-dimensional IMFs as fingerprint features.And for the characteristics of communication signals,improved dApEn and SpApEn algorithms are proposed.MIX process and Logistic map verify the improvement of the relative consistency and noise-immunity of the improved algorithm.With SVM classifier,the individual recognition simulation experiment is carried out in AWGN environment.The results show that the recognition probability and the recognition noise-immunity of the individual recognition system based on ApEn fingerprint features are significantly improved compared with those based on HHT.(5)A novel fingerprint recognition algorithm based on manifold learning and sparse description is proposed.Firstly,the bispectrum is used as the individual fingerprint.The data reduction dimension part,using the LPP algorithm in the manifold learning,takes the supervised learning and the category information to obtain the 2DDSLPP algorithm,which effectively reduces the data dimension and keeps the individual fingerprint information.The matching recognition part,based on the K-Sparse description of sparse description of fingerprints,improve the efficiency compared with traditional sparse representation method.Simulations of individual recognition were also carried out for multiple cases.The results show that both K-SDC and K-FSC have better recognition performance than the GDC method,and prove the effectiveness of the sparse description in individual fingerprint recognition.The individual recognition system not only has good recognition probability and good identify noise-immunity,and have the best performance in identify consistency,can adapt to different recognition scene with different signal modulation mode.