摘要
支持向量数据描述(support vector data description,SVDD)常用于实现目标类样本充分、非目标类样本多样化的两类分类。在雷达目标识别应用中,SVDD分类性能随样本噪声增加迅速下降。为了解决这个问题,通过深入分析SVDD抗噪性能差的原因,提出了基于自适应SVDD的雷达目标分类方法。该方法利用接收机工作特性曲线建立信噪比与分类最优超球半径的关系模型,在目标分类过程中,针对不同信噪比自适应选择分类判决门限。仿真实验表明,相比于常规SVDD方法,自适应SVDD方法大大提高了低信噪比下目标分类性能。
Support vector data description（SVDD） is usually used to distinguish two classes that the target class can obtain sufficient samples and the non-target class involves various kinds of objects.However,while used in the field of radar target recognition,the classification ability of SVDD rapidly weakens with an increase in noise energy.In order to deal with such problem,the reason why noise results in the weakening of SVDD is particularly described,and a method of radar target classification based on adaptive SVDD is proposed.The proposed method constructs an adaptive model between signal to noise ratio（SNR） value and optimal hypersphere radius using a receiver operating characteristic curve,which can adaptively choose the decision thresholds of different SNR values in target classification.The experiment results demonstrate that the adaptive SVDD algorithm greatly improves the classification performance of targets in low SNR condition compared with the classical SVDD algorithm.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2011年第2期253-258,共6页
Systems Engineering and Electronics
基金
国家自然科学基金(60572138)
重点实验室基金(9140C8001020901)资助课题
关键词
支持向量数据描述
目标分类
自适应
超球半径
support vector data description（SVDD）
target classification
adaptive
hypersphere radius
