Object Classification of UWB Responses Using ST-CNN

被引:0
作者
Ko, Seok-Kap [1 ]
Lee, Byung-Tak [1 ]
机构
[1] ETRI, Honam Res Ctr, Energy Syst Res Sect, Daejeon, South Korea
来源
2016 INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGY CONVERGENCE (ICTC 2016): TOWARDS SMARTER HYPER-CONNECTED WORLD | 2016年
关键词
UWB; CNN; Machine Learning; Deep Learning; S-transform; classification;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
UWB response includes unique characteristics of reflecting objects. Because the response is the combination of many distortion, resonance, and multi-paths, the object classification of UWB response is difficult. In this paper, we propose an object classification method using S-transform and convolution neural network. S-transform converts time series data of UWB response to frequency-time domain which convolutional neural network can learn and classify.
引用
收藏
页码:794 / 796
页数:3
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