Study On Subtle Features Of Individual Communication Transmitters

Identification of individual communication transmitters is one of the hotspots in the field of communication countermeasure. Researches on the subtle features of communication transmitters have gained some progress with the increasing of new techniques used in signal processing. Information such as signal features, types and individual features can be derived from the research on the subtle features maintained from the transmitters, which is an important measure of decoding, data fusion, pattern recognition, object tracking and situation analyzing. Analyzing of the subtle features from the individual transmitters has played an important role in either the military or civil field for a long time. However, differences of the technical parameters between individual transmitters are fine, thus analyzing of subtle features is rather difficult, which is a little different from that of radar.In this paper, the characteristics of stable signals, such as carrier frequency offset, modulation parameters and stray features of the transmitters, and their significances in the course of the identification are both studied systematically. Thereby, a novel feature extraction and classification algorithm based on polyspectral kernel function is presented, in which polyspectral theory and kernel technique are combined, and support vector machines (SVMs) are chosen as the classifiers. Feature sets are reduced by the neighborhood rough set algorithm. Furthermore, two-class classifiers and combined classifiers are designed based on the discussion above. The proposed algorithms are verified to be efficient using the measured radio data. Besides that, researches on the subtle features of the frequency-hopping spread spectrum signals are carried out.The main contributions of this paper are concluded as follows.1. Evaluation method of the significance of the features based on the neighborhood rough set is studied. General signal characteristics including carrier frequency offset, modulation parameters, and stray features, and high order statistical characteristics of the transmitters are studied in this paper. The results show that general signal characteristics play an important role in the signal identification. We can get the significance of every feature in classification on the basis of the experimental data. It is proved that the significance of every feature is obviously different, thus they should be treated distinctively. The distinctive treatments of every feature and thinking of the weight are well reflected in the paper.2. To address the curse of dimensionality caused by the higher order spectrum used in classification, a new feature extraction algorithm based on poly spectral kernel function is presented. The method can reduce the numbers of the support vectors effectively, and shorten the training and classifying time in comparison to the bispectral analysis (including integral bispectral and selected bispectral). Moreover, it can improve the identification rate.Then how to choose the order of the polyspectral kernels is studied. The neighborhood rough set theory is proposed for evaluating the significances of the order of the polyspectral kernels, which raises a novel weighted polyspectral kernel. The experiment shows that better and stable classification rate can be achieved.4. The selection of the signal feature and the design of the classifiers are discussed. Data set reduction algorithm based on the neighborhood rough set theory is proposed in the selection of the signal features. An optimized feature subset can be obtained through reducing the dimension of the subtle feature set defined in the paper. In the design of the SVM classifiers, the significance of every attribute is used as the weight to construct weighted feature in order to improve the identification rate. And the weighted voting combination of multi-classifiers are designed on the basis of the significance of every attribute. The experimental results show that the efficiency of the weighted voting multi-classifiers is better than the single classifier.5. Subtle features of the frequency hopping signals are studied. Wavelet packet decomposition and reconstitution are used to constrain the cross-term brought about by WVD method. Then the extraction of the feature from the frequency hopping signals is carried out. Wavelet kernels function algorithm is put forward based on the principle of kernel function. Relevant parameter optimizations are discussed through the experiments on the measured radio signals. The experimental results show that features of FH signals extracted by the way of Wavelet constrained WVD cross-term have a certain clustering performance. A pretty good identification can be achieved if the appropriate parameter is chosen.