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A method for feature extraction based on SVD and machine learning

机译:一种基于SVD和机器学习的特征提取方法

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By studying the shortcomings of feature, which extracted from Radar-Cross Section(RCS),using mathematical and statistical method, using the idea of extracting abstract features in image recognition and speech recognition by artificial intelligence for reference.This paper explores the possibility of extracting abstract features of target's RCS sequence, and proposes an abstract feature extraction method of RCS sequence based on singular value decomposition(SVD) feature decomposition. Because of the poor interpretability of abstract features, four different machine learning algorithms are used to classify the extracted Abstract features. The experimental results show that the machine learning algorithm can classify different types of spatial objects with high accuracy, which shows that the RCS features of different spatial objects can be characterized by absfract features.
机译:通过研究从雷达横截面(RCS)中提取的特征的缺点,使用数学和统计方法,使用人工智能提取图像识别和语音识别中的抽象特征的想法进行参考。本文探讨了提取的可能性摘要目标RCS序列的特点,并提出了一种基于奇异值分解(SVD)特征分解的RCS序列的抽象特征提取方法。由于抽象特征的可解释性差,因此使用四种不同的机器学习算法来分类提取的抽象功能。实验结果表明,机器学习算法可以高精度地分类不同类型的空间物体,这表明不同空间物体的RCS特征可以通过缺陷特征来表征。

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