Research On Mechanism And Methodology Of Specific Emitter Identification

Specific Emitter Identification (SEI) refers to designating the unique transmitter of a given signal, using only external feature measurements, by comparing those features with a library. With the improving ability of the parameter agileness in radar and communication systems, signal identification utilizing conventional parameter measurement techniques is difficult to satisfy the demand of ELectronic INTelligence (ELINT) systems. Specific Emitter Identification utilizing the signal's unintentional modulation then become an important trend of electronic intelligence development.The existing research on SEI has two main problems. One is the absent of research on the mechanism of fingerprints. As a result, the obtained features are easy to fail when the intentional modulation changes. The other problem is the insufficient of the feature measurability. The background noise, nonlinearity of the receiver, multipath effect may all decrease the accuracy of classification.This paper is aiming to solve the above two problems. The content of the dissertation is as follows.Based on the equivalent circuit model of RF self-excitation device, such as magnetron, a SEI method for self-oscillatory transmitter is proposed. This model-based method defines a set of fingerprints, which expand the traditional features, and integrate the relationship of the signal's instantaneous frequency and instantaneous band. The method can extract features from the middle of the pulse,so it can cover the shortage of the traditional feature extraction methods only using the rising and trailing edge of the pulse. A Particle In Cell (PIC) simulation method for fingerprints analysis is also studied. The fingerprints of the previous model is proved by the simulation of the free and the controlled excitation process.A series of SEI method utilizing the power amplifier(PA)'s nonlinear property are developed. (1)When the driven signal is sine signal with changeable power (in different observation), an approximate harmonic constraint feature is obtained. A more general method based on Wu method is also presented. (2) When the driven signal is narrow band with changeable intentional modulation, a Multi-Channel Correlation Fingerprinting(MCCF) method is proposed. From amplifier's Taylor series model, the expressions of the Carrier Component (CC) and the Harmonic Component (HC) of the output signal is derived. Then a least square algorithm is deduced by substituting the CC into the HC as an approximation of the driven signal. The observation condition and estimation CRLB of MCCF are provided. The fingerprints of MCCF depend on the Taylor series model, and are independent of the input signals, so they are inherent. The experiment of four FM broadcast emitters in Changsha area shows that, the MCCF method works well, even when the HC is 60dB to 80dB smaller than the CC.(3) When the driven signal is wide band with changeable intentional modulation, a subspace comparison based fingerprinting method is proposed. Two MIMO models of PA's Volterra series model are given, based on which the parameter subspace can be derived. To avoid the dimension explosion of the Volterra series model, different PA systems is identified by comparing the subspace directly. The independent and measurable properties of the method are given, and the principle of the method is verified by the simulation experiments.A Cross-Correlation Integral SEI method(CCI-SEI) is developed to identify the amplifier's unintentional modulation of the continuous signal. The CCI algorithm is used to compare the reconstructed vectors of the two signal with high degree of accuracy in the phase space. Before the comparison, a re-sample step is taken to make the reconstructed vectors' distribution more smoothly. Also a statistical test method and a theoretical analysis method are developed to choose appropriate reconstruct parameters. CCI-SEI borrows the ideas from the work of T.L.Carroll, so Carroll's Phase Space Difference (PSD) method is introduced, the shortage of PSD method in low SNR is analyzed too.The experiment results show that CCI-SEI has better performance than the PSD method.Studies the utility of Kernel Principle Component Analysis (KPCA) method in SEI application. Two kernel functions are developed to extract fingerprints. The Instantaneous Frequency RBF kernel(IF-RBF) and its estimator can be used in the feature extraction of frequency modulation signals with Tiny Instantaneous Frequency Difference (TIFD). This kernel uses the IFD of the two signals, so the IFD estimator's CRLB is derived, which can explain the reason of the improvement. Another proposed kernel function is Asymmetric Frobenius Subspace Kernel Function (AF-SKF), which can be used in the feature extraction of pulse groups. The rationality of the definition, the property and the calculation method are given. The derivation also shows that, the symmetric subspace kernel function WS-SKF is just a special case of AF-SKF. After the discussion of kernel function, a SVD Updating based KPCA algorithm (SVDU-KPCA) is derived. The simulation experiment proofs it.A Frequency Domain Generalized Observation Model (FDGOM) for SEI in multipath environment is proposed. Combined with the hypothesis testing theory of signal classification, three robust hypothesis testing methods are studied: Signal Dependent Contamination Parameter (SDCP) method, random signal robust hypothesis testing method and the weak random signal asymptotic robust testing method. The first two methods base on the Huber's contamination model, and are suit for the case that the interference of multipath is bigger than the interference of noise. The derivation of asymptotic robust testing shows that asymptotic testing method will degenerate into a simple likelihood ratio testing method when the interference of noise is obviously bigger than the multipath.