A New Method for Trajectory Extrapolation of Firing Position Reconnaissance and Calibration Radar based on SVM
Zhang Xian · Fire Control and Command Control · 2007
SVM is a new machine learning method with sufficient theoretical motivation,and it is mainly used for classification recognition and regression modeling in terms of finite samples.In this paper,each trajectory is viewed as a training sample,and SVM regression method is applied to induce the law of the trajectory falling points.Therefore,the falling points can be predicted.One of the most important contribution in this paper is that we introduce SVM into the field of trajectory extrapolation of radar,and the another contribution lies in we apply SVM using many techniques,which includes constructing representative samples and unifying the dimension of input space.The simulation experiments demonstrate that SVM can improve the accuracy of trajectory prediction while reduce the prediction time.