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

页码：794 / 796

页数：3

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