期刊
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷 58, 期 5, 页码 3574-3587出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2019.2958178
关键词
Angle sensitivity; convolutional neural network (CNN); human motion classification; micro-Doppler
类别
资金
- National Natural Science Foundation of China [61520106002, 61731003, 61901049]
- Japan Society for the Promotion of Science (JSPS) KAKENHI [15K18077, 15KK0243]
- Japan Science and Technology Agency (JST) the Precursory Research for Embryonic Science and Technology (PRESTO), Japan [JPMJPR1873]
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.
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