Research On Modulation Classification And Recognition Algorithm Of Communication Signals

Communication technologies are developed at a very fast speed, and it is especially represented by the changes and improvements of various communication modulation types. Thus, the algorithms to classify and recognize communication signal modulations are developed. Because the signal environment is becoming more crowded, conventional methods can not meet the needs of real situations, which puts forward high demands for modulation classification and recognition of digital communication signals.Modulation classification and recognition is very significant for both cooperative and non-cooperative field. In cooperative field, modulation classification and recognition is the technical base for universal receiver based on software radio and intelligent receiver based on cognitive radio, and they are also the bottleneck and kernel. Besides, governments apply modulation classification and recognition to implement effective wireless frequency management, surveil civil signals and so on. In non-cooperative field, modulation classification and recognition plays a key function. In the future, wars mainly depend on the superiority of information, which makes communication more outstanding. Anti-communication is the focus in electromagnetic battle field.Anti-communication requires intercepting and capturing enemy's signals and then getting the information embedded in signals. Modulation classification and recognition is the base of this process. In different kinds of wireless communication signal types, satellite communication signal occupies a big proportion. Thus, modulation classification and recognition for satellite communication signal has very high practical values. Because of the complexity of satellite wireless communication environment, especially the unpredictability of the battlefield, performances of traditional modulation classification and recognition algorithms become worse and can not meet the demands of wars. Thus, modulation classification and recognition algorithms for non-ideal situations have been paid much attention.Based on this background, the problem about modulation classification and recognition algorithms for wide range SNR and fading channels are deeply and systematically developed in this dissertation.Firstly, blind SNR estimation is investigated. SNR is an important criterion of signal quality and it is the groundwork for the following bind equalization and modulation classification and recognition. A blind SNR estimation algorithm is proposed based on signal subspace and combined information criterion. It not only fits for small sample estimation but also enlarges the range of estimation. What's more, it can be applied in both AWGN channels and fading channels.Secondly, blind equalization is studied in details to reduce the affection of non-ideal channels. Inter-satellite link channel and satellite-land channel are analyzed and their mathematical models are presented. For blind equalization, super-exponential method (SEM) is improved and a novel robust super-exponential method (NRSEM) is proposed. In the method, noise power of intercepted signals is estimated which is used to decrease the influence of noise. Then second order and fourth order comulants are combined to equalize channels. Since noise is decreased effectively, the drawback of bad convergence property in low SNRs of the original SEM is overcame,and the convergence speed is promised.Thirdly, comparisons are made between SVM and neural networks and the results show that SVM has not only simple structure but also strong generalization ability. It avoids over-fitting, under-fitting and local minimum in neural networks. Three modulation classification and recognition algorithms by virtue of support vector machine (SVM) are presented aiming at the problems existing in present modulation recognition algorithms. The first algorithm is SVM fuzzy network. It uses several classifiers and fuses recognition results by a new fuzzy integral, which can widen the range of SNR for modulation recognition. Especially, it has good performance in low SNRs. The second algorithm is adaptive modulation classification and recognition based on SVM. It employs the SNR estimation in the second chapter to select suitable classifier to classify signals. This algorithm only uses single classifier to realize the modulation recognition in wide SNR range. The third algorithm is modulation recognition based on wavelet and wavelet SVM (WSVM). It puts forward an improved classifier training method, and also widen the SNR range of modulation classification with a single classifier. The effects of carrier frequency errors on the classification success rate are analyzed and computer simulations show that carrier frequency errors in a certain range can be accepted in modulation classification and recognition algorithms. What's more, combined classifiers have better capability to overcome the affections of carrier frequency errors compared with single classifiers.Lastly, a modulation classification and recognition test system is emplyed and a simplified modulation classification and recognition algorithm is transferred to the hardware board. The performance on the hardware board is deeply investigated using the signal source generator E4438. Furthermore, the modulation recognition results of an in-orbit model satellite are given using modulation classification and recognition test system and remote sensing and controlling earth station in Harbin Institute of Technology, which proves the engineering validity of the algorithm.