南京大学学报（自然科学版）2017，Vol.53Issue(5)：984-989,6.DOI:10.13232/j.cnki.jnju.2017.05.018
一种改进的BP-Adaboost算法及在雷达多目标分类上的应用
An improved algorithm of BP-Adaboost and application of radar multi-target classification
摘要
Abstract
BP-Adaboost based classification algorithm has a promising performance in the field of Radar target classification.However,due to the increase of the volume of training samples and testing samples,traditional BP-Adaboost based algorithm OvR (One vs.Rest)requires an amount of time.Therefore,this paper proposes an improved BPAdaboost algorithm to solve the issue of Radar multi-target classification,which can improve accuracy and solve the time issue that traditional algorithm will encounter in multi-class classification effectively.Specially,we preprocess the initial categories in binary,then several BP neural networks are boosted up to form a new strong classifier,in which process we keep adjusting the distribution of training samples and weight of weak classifiers by modifying the loss function and the number of output nodes.Experiments using the range profile datasets have been demonstrated that the proposed scheme can raise accuracy up to 5％ ～ 10％ compared to single BP neural network,and significantly improves the time issue efficiently compared to traditional algorithm OvR,which can save 1/2～2/3running time under the same condition.关键词
Adaboost/雷达高分辨率距离像/多分类/BP神经网络Key words
Adaboost/high resolution range profile(HRRP)/multi-class/BP neural networks分类
信息技术与安全科学引用本文复制引用
李蓓,张兴敢,方晖..一种改进的BP-Adaboost算法及在雷达多目标分类上的应用[J].南京大学学报（自然科学版）,2017,53(5):984-989,6.基金项目
江苏省基础研究计划(BK20151391) （BK20151391）
