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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