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Support Vector Machines Learning Algorithm and Its Application in Radar Target Recognition

Author: SunFaSheng
Tutor: XiaoHuaiTie
School: National University of Defense Science and Technology
Course: Electronics and Communication Engineering
Keywords: Support Vector Machine Radar Target Recognition K-nearest neighbor Incremental learning Multi - class classification
CLC: TN957.5
Type: Master's thesis
Year: 2007
Downloads: 181
Quote: 1
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Support Vector Machine (SVM) is a new pattern recognition method based on statistical learning theory developed to solve the small sample size, nonlinear and high dimensional pattern recognition and other issues unique strengths , overcome nerves many of the shortcomings of the classified network and traditional statistical classification method with higher generalization performance . Therefore , SVM has good prospects for radar target recognition . But as an emerging technology , SVM methods , there are a lot of shortcomings , such as large-scale training data more slowly , and does not support incremental learning and multi- classification problems , its application by a lot of restrictions . To this end , the paper SVM learning algorithm and apply it to radar HRRP target recognition (HRRP) , the main work includes: 1 . Propose a fast training algorithm for SVM based on K nearest neighbor (KNN- SVM). Extracted using the K nearest neighbor thought most likely to become support vector border vector set , and then to the border vector set for the training sample set , standard SVM learning . Experimental results show that the algorithm under the premise of ensuring the classification ability , effectively improve the training speed of SVM . 2 . A fast SVM incremental learning algorithms ( KNN ISVM ) , based on the K - nearest neighbor . Using K - nearest neighbor thinking extraction of the border vector set instead of the training sample set for SVM training , significantly reduce the number of samples involved in the training , the SVM training speed is greatly increased . Border vector set contains all useful training sample classification ability of SVM classification has been guaranteed. The experimental results show the effectiveness of the algorithm . 3 . A SVM multiclass classification based on nuclear hierarchical clustering algorithm ( KHC- SVM ) . Use of a reasonable nuclear hierarchical clustering algorithm to generate a hierarchy of binary tree , then KNN-SVM construct a binary tree nodes within the optimal hyperplane , not only to improve the speed of SVM training and can further improve the speed and accuracy of SVM classification . Experimental results show that the algorithm has higher generalization ability . The papers mentioned in the algorithm used in the identification of radar HRRP achieved fairly good results .

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CLC: > Industrial Technology > Radio electronics, telecommunications technology > Radar > Radar equipment,radar > Radar receiving equipment
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