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首页> 外文期刊>Journal of Electromagnetic Waves and Applications >Comparisons of four feature extraction approaches based on Fisher's linear discriminant criterion in radar target recognition
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Comparisons of four feature extraction approaches based on Fisher's linear discriminant criterion in radar target recognition

机译：四种基于Fisher线性判别准则的特征提取方法在雷达目标识别中的比较

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摘要

In this paper, the goal is to produce a highly separable and small-dimensional feature set for improving the target recognition strategy called Invariant Feature-based Method (IFM), which uses the conventional principal component analysis to reduce redundant information and feature space dimension. To meet this end, the principal component analysis is replaced with Fisher's linear discriminant criterion, originally developed for discriminating various patterns. Among the various versions of Fisher's criterion, four computationally efficient techniques including classical linear discriminant vectors (CLDV), classical linear discriminant vectors with whitening process (CLDVW), and weighted pairwise Fisher criteria vectors (WPFCV), weighted pairwise Fisher criteria vectors with whitening process (WPFCVW) are considered. It is shown that among the four techniques, CLDVW and WPFCVW outperform CLDV, WPFCV, and the conventional principal component analysis. In addition, an optimum number of feature dimension for Fisher's criterion combined with IFM is experimentally derived, and associated theoretical background is discussed.
机译：在本文中，目标是产生一种高度可分离的小维特征集，以改进目标识别策略，称为不变特征基方法（IFM），该方法使用常规的主成分分析来减少冗余信息和特征空间维。为了达到此目的，主成分分析被替换为Fisher线性判别准则，该准则最初是为区分各种模式而开发的。在费舍尔准则的各种版本中，四种计算效率高的技术包括经典线性判别向量（CLDV），具有白化过程的古典线性判别向量（CLDVW）和加权成对的Fisher准则向量（WPFCV），具有成白过程的加权成对的Fisher准则向量（WPFCVW）被考虑。结果表明，在四种技术中，CLDVW和WPFCVW优于CLDV，WPFCV和常规主成分分析。另外，通过实验推导了结合Fisher准则的Fisher准则的最优特征维数，并讨论了相关的理论背景。

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