基于非相关判别邻域保持投影的毫米波雷达目标识别

罗磊; 李跃华

doi:10.3724/SP.J.1146.2009.01534

基于非相关判别邻域保持投影的毫米波雷达目标识别

doi: 10.3724/SP.J.1146.2009.01534

罗磊,
李跃华

基金项目:

国家部委基金资助课题

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- 文章访问数: 3862
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- 被引次数: 0
出版历程
- 收稿日期: 2009-12-01
- 修回日期: 2010-03-15
- 刊出日期: 2010-11-19

Uncorrelated Discriminant Neighborhood Preserving Projections for Millimeter Wave Radar Target Recognition

Luo Lei,
Li Yue-Hua

摘要

摘要: 该文基于流形学习的思想，综合线性判别分析(LDA)及邻域保持投影(NPP)算法的优势，提出了一种新的流形学习算法，即非相关判别邻域保持投影(Uncorrelated Discriminant Neighborhood Preserving Projections, UDNPP)。该算法在保持了邻域几何结构的同时最大化了类间距离，并通过引入一非相关约束条件使得提取的特征向量具有非相关性，减少了冗余信息的干扰。毫米波雷达目标识别实验结果表明，非相关判别邻域保持投影算法相对其它流行的学习算法有更好的性能。
- 目标识别 /
- 毫米波雷达 /
- 特征提取 /
- 流形学习 /
- 类间距离 /
- 非相关约束
Abstract: A new algorithm named Uncorrelated Discriminant Neighborhood Preserving Projections (UDNPP) is proposed based on manifold learning. And UDNPP algorithm includes the advantages of Linear Discriminant Analysis (LDA) and Neighborhood Preserving Projections (NPP). Actually, UDNPP attempts to preserve the geometry of neighborhoods, while maximizing the between-class distance. Moreover, the features extracted are statistically uncorrelated by introducing an uncorrelated constraint. Thus the interference from redundant information are reduced. The experimental results from millimeter wave radar target recognition show that UDNPP algorithm can give competitive results in comparison with current popular algorithms.
- Target recognition /
- Millimeter wave radar /
- Feature extraction /
- Manifold learning /
- Between-class distance /
- Uncorrelated constraint

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