True-False Target Recognition In High Resolution Radar  Based on One-Class SVM

The problem of high resolution radar target detection is taken as the problem of true-false target recognition in this paper. In allusion to the shortcoming of the existing high resolution radar target detection algorithm, and by borrowing ideas from the dealing with novelty problem, one-class SVM is introduced to high resolution radar target detection for the first time. That can provide a new idea for solving high resolution radar target detection problem. At the same time, in allusion to the incompleteness of one-class SVM in describing data domain, and combing the data distribution characteristics of the high resolution radar object, a cluster one-class SVM model is proposed. It conducts training positive kind based on clustering, uses several small spheres instead of previously one big sphere, and gives more accurate description of the data domain. At last, in allusion to the condition of the existing several kinds of true targets, a method that deals with every single kind of true target separately is proposed to satisfy with the need of the succeeding true target type identifying. Experiments with radar raw data show the validity of this algorithm.