Omnidirectional motion classification with monostatic radar system using micro-Doppler signatures

Yang, Yang, Hou, Chunping, Lang, Yue, Sakamoto, Takuya, He, Yuan, and Xiang, Wei (2020) Omnidirectional motion classification with monostatic radar system using micro-Doppler signatures. IEEE Transactions on Geoscience and Remote Sensing, 58 (5). pp. 3574-3587.

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Abstract

In remote sensing, micro-Doppler signatures are widely used in moving target detection and automatic target recognition. However, since Doppler signatures are easily affected by the moving direction of the target, prior information of aspect angle is essential for spectral analysis. Thus, a micro-Doppler-based classifier is considered to be "angle-sensitive." In this article, we propose an angle-insensitive classifier for the omnidirectional classification problem using the monostatic radar through a proposed new convolutional neural network. We further provide a sensible definition of "angle sensitivity," and perform experiments on two data sets obtained through simulations and measurements. The results demonstrate that the proposed algorithm outperforms both feature-based and existing deep-learning-based counterparts, and resolve the issue of angle sensitivity in micro-Doppler-based classification.

Item ID: 63192
Item Type: Article (Research - C1)
ISSN: 1558-0644
Keywords: Angle sensitivity, convolutional neural network (CNN), human motion classification, micro-Doppler
Copyright Information: © 2019 IEEE
Funders: National Natural Science Foundation of China (NNSFC), Japan Society for Promotion of Science (JSPS)
Projects and Grants: NNSFC Grant 61520106002, NNSFC Grant 61731003, NNSFC Grant 61901049, JSPS KAKENHI under Grant 15K18077, JSPS KAKENHI under Grant 15KK0243, JSPS Grant JPMJPR1873
Date Deposited: 20 May 2020 07:45
FoR Codes: 40 ENGINEERING > 4013 Geomatic engineering > 401304 Photogrammetry and remote sensing @ 0%
40 ENGINEERING > 4006 Communications engineering > 400607 Signal processing @ 100%
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