Robust PCA micro-doppler classification using SVM on embedded systems

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Robust PCA micro-doppler classification using SVM on embedded systems

机译：在嵌入式系统上使用SVM进行稳健的PCA微多普勒分类

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摘要

In this paper, a novel feature extraction technique for micro-Doppler classification and its real-time implementation using a support vector machine classifier on a low-cost, embedded digital signal processor are presented. The effectiveness of the proposed technique is improved through exploitation of the outlier rejection capabilities of robust principal component analysis (PCA) in place of classic PCA.

机译：本文提出了一种新的微多普勒分类特征提取技术及其在低成本嵌入式数字信号处理器上使用支持向量机分类器的实时实现。通过利用鲁棒主成分分析（PCA）代替经典PCA的异常排除功能，提高了所提出技术的有效性。

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《Aerospace and Electronic Systems, IEEE Transactions on》 |2014年第3期|2304-2310|共7页
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正文语种 eng
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关键词
Digital signal processing; Feature extraction; Legged locomotion; Principal component analysis; Radar; Support vector machines; Training;

机译：数字信号处理;特征提取;移动性;主成分分析;雷达;支持向量机;训练;

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7. Robust PCA for micro-doppler classification using SVM on embedded systems [O] . Zabalza, J., Clemente, C., Di Caterina, G., 2014

机译：在嵌入式系统上使用SVM进行微多普勒分类的稳健PCA

Robust PCA micro-doppler classification using SVM on embedded systems

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