CSI-based Vehicle target recognition and Collaborative CNN with Data-Importance-Aware

被引：5

作者：

Li, Shuo ^{[1
]}

Liu, Dingquan ^{[1
]}

Liu, Xin ^{[1
]}

Ma, Yunfei ^{[1
]}

Gu, Xin ^{[2
]}

Fan, Yunsheng ^{[2
]}

Wang, Pingping ^{[2
]}

Lu, Yao ^{[2
]}

Liu, Bowen ^{[2
]}

机构：

[1] Changsha Univ Sci & Technol, Sch Elect & Informat Engn, Changsha, Peoples R China

[2] Cent South Univ, Sch Comp Sci, Changsha, Peoples R China

来源：

2022 IEEE 25TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC) | 2022年

基金：

中国国家自然科学基金;

关键词：

Collaborative Machine Learning; Target recognition; channel state information;

D O I：

10.1109/ITSC55140.2022.9922320

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The introduction of orthogonal frequency division multiplexing (OFDM) and multiple input multiple output (MIMO) technology enables the new generation of the Internet of Vehicles to have the ability to integrate wireless communication and target tracking. Based on the new Internet of Vehicles, a cooperative convolutional neural network with dataimportance-aware (DIA) is proposed.We use wireless channel state information (CSI) to construct a micro Doppler feature data set. A distributed collaborative machine learning method of DIA is proposed to realize the target recognition of the vehicle. Experimental results show that the vehicle recognition accuracy of the proposed method is higher than 95%, and the convergence speed of the machine learning model is improved.

引用

页码：2069 / 2074

页数：6

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