基于LLE的分类算法及其在被动毫米波目标识别中的应用

罗磊; 李跃华

doi:10.3724/SP.J.1146.2009.00877

基于LLE的分类算法及其在被动毫米波目标识别中的应用

doi: 10.3724/SP.J.1146.2009.00877

罗磊,
李跃华

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- 被引次数: 0
出版历程
- 收稿日期: 2009-06-12
- 修回日期: 2010-02-09
- 刊出日期: 2010-06-19

LLE-Based Classification Algorithm and Its Application to Passive Millimeter Wave Target Recognition

Luo Lei,
Li Yue-hua

摘要

摘要: 该文针对模式识别中的单类分类问题，根据LLE算法思想，考虑数据分布的低维流形，提出了一种单类分类算法。基于流形学习算法发现了被动毫米波信号的短时傅里叶谱中低维流形的存在，并讨论了其特性。将新算法应用于被动毫米波金属目标识别，相对目前流行的分类算法，取得了更好的效果，且算法对输入参数不敏感，在数据混叠程度较高时仍有很好的鲁棒性。
- 目标识别; 毫米波; 流形学习; 局部线性嵌入; 单类分类
Abstract: A new one-class classification algorithm is put forward based on the idea of Locally Linear Embedding (LLE) and the low dimensional manifold of data distribution, for the issue of one-class classification of pattern recognition. The low dimensional manifold the short-time Fourier spectrum of passive millimeter wave (MMW) detector echo signal is found, which characteristics are explored as well. The algorithm is more efficient than present popular one-class classification algorithm when applied to passive MMW metal target recognition. Moreover, it is not sensitive to input parameters and is robust to data aliasing.

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