The Algorithm Design For Support Vector Machines With Application To Hrr Target Recognition

The high resolution range profile (HRRP) obtained by the high resolution radar contains abundant structure signatures of the target and embraces unique advantages in the field of Radar Automatic Target Recognition (RATR).The Support Vector Machines (SVMs) founded on the Structural Risk Minimization (SRM) principles has many good properties, such as better generalization, small training samples, nonlinear, no local minima, etc, which make it become a powerful learning machines in the application of ATR based on HRRP.In this background, the multiobjective parameter selection algorithm and the multistage selective ensemble algorithm for SVMs along with their application in the high resolution radar target recogniton have been researched by the paper.The main contents of the paper are arranged as follows:In chapter one, the background and significance of the subject, the progress of the related technology and the current status of the research are briefly introduced. Chapter two gives an extensive materials about the basic principles of the high resolution range profile and the support vector machines. The obtaining of HRRP is deduced,and the target-aspec and time-shift sensitivity problems are conclued to analyze the features of the HRRP samples, then the theory foundation, the basic algorithms and the characters of SVMs are detailedly stated, which give a comprehensive bases for the design and application of the algorithm in the successive chapters.In chapter three, the design of the multiobjective parameter selection algorithm for SVMs is studied.Based on the simulation, the necessary of optimal parameter selection is demonstrated by their direct influence on the algorithm's generalization ability. The traditional parameter selection methods are usually conducted through a single generalization error bound , and that seems insufficient according to the results of our experiments. From our view, the issue of parameter selection should be treated as a multiobjectice optimization problem. A multiobjective parameter selection algorithm based on the nondominated sorting genetic algorithm (NSGA-II) is proposed in the paper, and the experiments'results show that compared with single-objective parameter selection algorithm the multiobjective parameter selection algorithm can obtain a better moderate parameter values which ensure a higher correct recognition rate.In chapter four, the design of the multistage selective ensemble algorithm for SVMs is investigated. Ensemble Learning can ensure a better generalization ability for a learning machine, and the selective ensemble can hugely reduce the number of the members that constitute a ensemble without lossing or even improving the ensemble's generalization ability, which is a very helpful property in the application to the ATR based on HRRP.The paper proposes a multistage selective ensemble algorithm for SVMs based on genetic algorithm (GA). The simulation results indicate that the correct recognition rate achieved by the algorithm is higher than the bagging ensemble and a single SVMs while just a less number of ensemble members have been used.Finally, the dissertation is concluded in chapter five. Several aspects for future work are also pointed out.