Research On Support Vector Machines Algorithm And Its Application In Radar Jamming Effect Evaluation

With the development of electronic technology, radar confrontation is playing an important role in modern warfare. Radar jamming is the main component of radar confrontation, and jamming effect is an important index of jamming equipment, which is showed as the decline degree of radar's performance before and after jamming. Jamming effect is influenced by many factors of radar, jamming and confrontation environment, and jamming effect evaluation is estimating the possible jamming effect according to influence factors. Scientific and rational evaluation of jamming effect takes great significance on the research of radar jamming/anti-jamming technology, and the development of radar and jamming equipment.The main evaluation methods of radar jamming effect include early evaluation factor methods, subsequent fuzzy synthetic evaluation methods and intelligent evaluation methods based on machine learning theory, such as artificial neural network (ANN). In intelligent evaluation methods, machine learning theories are used to get the relationship between jamming effect and influence factors by learning on samples get in radar jamming experiment. Because the intelligent evaluation is based on experiment samples and slightly influenced by artificial factors, it has been considered to be a very promising approach to solve radar jamming effect evaluation. From the perspective of intelligent evaluation, the evaluation of radar jamming effect can be considered as learning problem with limited samples, in this case the generalization ability of ANN would decline because of overfitting, while support vector machine (SVM) based on statistical learning theory is developed to solve the learning problem with limited samples, so it will be more superior that SVM is used to solve jamming effect evaluation problem. Because of the diversity of radar jamming, in this dissertation researches are focused on self-screening jamming of shipbased active jamming equipment to terminal guidance radar of anti-ship missile. Researches on proper SVM algorithms for the evaluation and prediction at wartime of radar jamming effect, and solving the relevant application problems of SVM are the main contents of this dissertation, detailed as follows:1. In support vector machine algorithm, the least squares support vector machine (LS-SVM) with low computational complexity was studied. To satisfy the offline learning requirement of jamming effect evaluation and online learning requirement to the samples with larger predicting error in jamming effect prediction at wartime, and avoid the shortcoming of standard LS-SVM that solution is nonsparse, two kinds of online LS-SVM were studied: the online LS-SVM based on refreshing inverse kernel matrix and the online LS-SVM based on sequence minimal optimization (SMO), the classification and regression algorithms of them were proposed. Both of them iteratively accomplish learning process by use of "predicting→incremental learning→decremental learning" idea. They can adaptively get sparse solution according to specific learning problem, not only can learn rapidly at offline problem, but also can be used in online learning problem.2. The relevant parameters selection and features selection of SVM were studied in succession. In parameters selection of SVM, according to the character that SVM's performance is multi-peak function of parameters, the differential evolution (DE) algorithm with strong global optimization ability was used to solve the parameters selection of SVM. Moreover, in machine learning area there are matters with high dimension inputs (features) and the features need to be selected. According to this case, parameters and features simultaneous selection based on DE algorithm was studied. Compared with the similar methods based on PSO, the DE-based parameters selection method and the DE-based simultaneous selection method not only is more rapid at optimization speed, but also have more powerful parameters and features selection ability.3. To get the training samples for intelligent evaluation, the measurement method of jamming effect in experiment was studied. On the base of analysis of terminal guidance radar's working process and the countering active jamming, and the combination of "time norms" and "efficiency norms", a comprehensive quantitative measurement method was proposed which use searching-time ratio and tracking-error ratio as measurement indexes. Jamming experiments were implemented by use of semi-physical simulation system at one of naval electronic warfare simulation center, results show that the measured jamming effect accords with theory and practice engineering rule, and the measurement method of radar jamming effect is effective.4. According to the quantitative requirement of radar jamming effect evaluation, it was regarded as a regression problem, and the jamming effect evaluation method based on LS-SVM regression was studied on the base of analyzing main influence factors of terminal guidance radar's active jamming effect. Semi-physical simulation experiments show that the proposed evaluation method can accurately evaluate jamming effect according to specific influence factors. Compared with the evaluation method based on ANN, the LS-SVM regression-based method has higher evaluation accuracy. In addition, the jamming effect prediction at wartime was tentatively studied. According to the fact that influence factors are not all knowable and predicting results aren't need very accuracy, jamming effect prediction at wartime was regarded as a classification problem, and the jamming effect prediction method at wartime based on LS-SVM classification was studied.