雷达目标一维距离像识别中的最优因式分析子空间法

周代英; 杨万麟

doi:10.3724/SP.J.1146.2006.00268

雷达目标一维距离像识别中的最优因式分析子空间法

doi: 10.3724/SP.J.1146.2006.00268

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出版历程
- 收稿日期: 2006-03-09
- 修回日期: 2006-09-13
- 刊出日期: 2007-10-19

Recognition of Radar Target Based on Optimal Factor Analysis Subspace Using Rangeprofile

摘要

摘要: 该文提出一种基于因式分析子空间进行特征提取的雷达目标识别方法。通过对目标训练样本集进行因式分析，在最大似然估计准则和最小错误分类率准则下建立最优因式分析子空间，利用因式分析子空间能够增强同类目标特征之间的相关性，提高同类目标特征的聚集度，从而改善目标识别性能。对三类飞机目标的仿真实验结果表明了方法的有效性。
- 雷达目标识别;一维距离像;最优因式分析子空间
Abstract: A novel approach of radar target recognition is proposed in this paper. For each target class, according to maximum likelihood rule and minimum classification error rate rule, an optimal factor analysis subspace is constructed by factor analysis on the training sample sets of target rangeprofiles. The optimal factor analysis subspace can produce an enhancement of correlation existing in the features of same target class. Such an enhancement provides higher clustering degree of the same class features. Thus, the factor analysis subspace is used to extract the feature of target for improving the accuracy of classification. The experimental results of three kinds of airplane targets demonstrate the efficiency of the approach proposed in this paper.

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