Few-Shot Learning for Human Motion Recognition Based on Carrier-Free UWB Radar

被引：0
|
作者：
Jiang L.-B. [1 ,3 ]
Zhou X.-L. [2 ,3 ]
Che L. [1 ,3 ]
机构：
[1] School of Computer and Information Security, Guilin University of Electronic Technology, Guilin, 541004, Guangxi
[2] School of Information and Communication, Guilin University of Electronic Technology, Guilin, 541004, Guangxi
[3] Key Laboratory of Wireless Broadband Communication and Signal Processing in Guangxi, Guilin University of Electronic Technology, Guilin, 541004, Guangxi
来源：
Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2020年 / 48卷 / 03期
关键词：
Carrier-free UWB radar; Discrete cosine transform (DCT); Few-shot learning; Human action recognition; Machine learning; Principal component analysis (PCA);
D O I：
10.3969/j.issn.0372-2112.2020.03.025
中图分类号：
学科分类号：
摘要：
As radar hardware platforms become smaller and cheaper, indoor radar-based motion recognition applications have become reality and can be implemented in low-cost devices with simple architectures.The carrier-free ultra-wideband (UWB) radar has extremely high resolution, which can capture the slight movement of the human motion and has a strong anti-jamming capability in indoor complex environments.Human motion recognition based on UWB radar compared to video-based also has the advantage of penetrating furniture, walls and protecting personal privacy.Aiming at the defects that the traditional time-frequency analysis method based on radar realizes the human motion recognition is time-consuming and poor real-time performance, the machine learning method is introduced to classify and recognize different types of human motions.The biggest difficulty in introducing machine learning methods for UWB radar human motion recognition is that there are only a few-shot of available UWB radar measured data samples.Therefore, a human motion feature extraction method based on principal component analysis (PCA) and discrete cosine transform (DCT) is proposed.And the support vector machine (SVM) optimized by the improved grid search algorithm is used for human motion recognition under few-shot samples.Finally, simulations experiments are performed based on measured data through three different schemes.Under the condition that there are only 5 groups of training data samples, the average recognition rate of human motion recognition can reach more than 96%. © 2020, Chinese Institute of Electronics. All right reserved.
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页码：602 / 615
页数：13
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