Communication Emitter Identification And Parameter Estimation

Communication emitter identification is a new issue in the field of communication reconnaissance in recent years, which is defined as designating the unique transmitter of a given signal, using only external feature measurements, by comparing those features with a library of clusters and selecting the cluster that best matches the feature measurements. With the features reflected on signal by the difference of the transmitter hardware, this issue focuses on seeking the source of the received signal, so the transmitter tracking and confirming can be realized. Communication emitter identification is paid much attention, which has very important significance in secure communication via wireless network, communication countermeasure and radio monitoring.It is urgent to solve the key problems of communication emitter identification. A series of theoretically and practically valuable algorithms are proposed, and the good performances of them are verified by simulation experiments with real data. The main contributions of this dissertation are concluded as follows:1. The communication parameter estimation is researched from three aspect: probability density distribution, SNR (signal to noise ratio) estimation, modulation recognition. In aspect of the probability density distribution problem, a probability distribution model of instantaneous frequency is researched for carrier signal. Based on analysis of the phase error distribution and approximate distribution, a approximate distribution functions are proposed, which has a good approximation effect, a simple form, and distinct physical meaning of parameters. The influence of the probability density distribution is analyzed in the case of different sampling rate or different SNR. Simulation results verify the probability distribution model of instantaneous frequency.In aspect of the SNR estimation, the relationship between SNR and high-order moment is analyzed for PSK and QAM signals. And a simple method is proposed to select the optimum moments by analyzing the SNR estimation performance with respect to different moments. The analysis shows the first-and second-order moments SNR estimator with the best performance for PSK signal, but the high-order moments SNR estimator has better performance for QAM signal. High-order moments SNR estimation method with Analytic expression is derived, which has low MSE (mean square error) and low complexity.In aspect of modulation recognition, linear statistics and directional data statistics are discussed respectively to recognize the modulation types of digital communication signal. For linear statistics, fourth-order cumulant and spectral peak features are used to recognize the modulation types. For directional data statistics, triangular moments are extraction based on the probability density distribution of instantaneous frequency and instantaneous phase. The results show that linear statistics and directional data statistics both can be used to recognize the modulation types. But directional data statistics requires a large amount of calculation for the cosine function, so the method is realized by means of iterative algorithm.2. For the difference reflected on signal by the difference of hardware under non-steady state, the transient characteristics are analyzed from three different angles. First, according to nonlinear characteristics of signal, the feature extraction method based on integral envelope is proposed. The transformation of integral envelope reflects the nonlinear characteristics in the signal envelope, and then the classification features are extracted by PCA (Principle Component Analysis). The results show that the method has good classification and anti-noise performance. Second, wavelet transform is used to extract features from the transmitters. The most discriminatory features are selected form a large number of wavelet transform features by genetic algorithms. Experiment results show that the method achieves good accuracy recognition rate in terms of a little features. Third, classification dependent adaptive time-frequency representation is proposed to research the nonlinear characteristics of transient signal. The method adaptively selects the parameter of Gaussian radial kernel function based on the ambiguity function. The method makes the separation degree maximum of the time-frequency distribution. The experimental results show that the method with the best performance, but the method needs long training time. Gradient ascend iterative algorithm is used to reduce the computational complexity and training time.3. For steady-state characteristics, the individual features are extracted by analyzing the characteristics of instantaneous frequency during the symbol hold time. A separation exponent is proposed to evaluate the separation performance of characteristic set in order to measure the classification ability of selective characteristics. This is a useful tool for selecting the correlative characteristics. And then classification dependent fractional Fourier transform is proposed to select the optimal order of fractional Fourier transform and distance measure by joint optimization the two parameters.4. For the frequency-hopping signal sorting, the frequency synthesizer phase noise of frequency-hopping radio is analyzed for its significant source of fine feature. The phase noise is the main reason of short-term frequency stability. The transient characteristics are extracted at frequency hopping time. it comes easy for the periodicity of frequency hopping signal. And frequency-hopping signal sorting is realized by the individual identification of frequency hopping signal.