Identification Of Spoofer Based On Nonlinear Of RF Power Amplifier

Spoofing is highly similar to the true signal in time, frequency and spatial domain, which will mislead the victim receiver. Compared with suppression jamming, the spoofing requires simpler hardware device, and it is harder to be detected and has better concealment. The essence and premise of anti-spoofing is that the receiver can correctly judge whether the received signal is spoofing or not. Motivated by the thought of the technology of Specific Emitter Identification, this dissertation studies feature extraction of spoofing and true signal from perspective of signal analysis and hardware modeling respectively, and then makes use of support vector machine and statistical detection method based on this feature vector to carry out classification of spoofing and true signal. Lastly, two nonlinear RF power amplifier development boards from a same manufacturer are utilized to data acquisition and experimental simulation. The main works of this thesis are as follows:1 The basic principles of Specific Emitter Identification and the research status of spoofing identification and feature extracting are introduced. From viewpoint of hardware, the nonlinearities of RF front end of a spoofer is analyzed. Then this dissertation introduces some common nonlinear indexes, and makes use of ADS design and simulates RF power amplifier to research the nonlinear features such as harmonic distortion and inter-modulation distortion so as to lay a foundation for spoofing identification.2 A method based on signal analysis is studied for spoofing identification. This dissertation firstly extracts and selects time-frequency Renyi entropy, singular value decomposed by wavelet coefficient, and bi-spectrum section as feature vectors. Based on these "fingerprint" features, a support vector machine classification algorithm is used, and a decision fusion at decision-making level is applied to fulfill recognition of spoofing and the true signal. Finally, with the data generated by the experimental platform, the simulation results shows that it is able to utilize more signal feature information through fusion method at decision-making level, and this method has a better performance than the method based on single signal feature.3 A method based on power amplifier models is studied for spoofing identification. Firstly, some common memory and memory-less model, such as Saleh model, Power Series model, Volterra series model, Hammerstein model, and Memory Polynomial model are used, to carry out nonlinear modeling on emitter and spoofer, and at the same time the coefficient vectors of these models as feature parameters of emitter and spoofer are extracted. Then, this dissertation uses statistical detection theory of likelihood ratio detection and NP detection to identify spoofer. This method avoids the loss of useful information upon selection of features and captures the essence of nonlinear mechanism. The experiments results show that the method based on nonlinear modeling of RF power amplifier can identify spoofing effectively.