TRTAR: Transmissive RIS-assisted Through-the-wall Human Activity Recognition
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913528159404032 |
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| author | Liu, Junshuo Huang, Yunlong Zhang, Jianan Xiong, Rujing Qiu, Robert Caiming |
| author_facet | Liu, Junshuo Huang, Yunlong Zhang, Jianan Xiong, Rujing Qiu, Robert Caiming |
| contents | Device-free human activity recognition plays a pivotal role in wireless sensing. However, current systems often fail to accommodate signal transmission through walls or necessitate dedicated noise removal algorithms. To overcome these limitations, we introduce TRTAR: a device-free passive human activity recognition system integrated with a transmissive reconfigurable intelligent surface (RIS). TRTAR eliminates the necessity for dedicated devices or noise removal algorithms, while specifically addressing signal propagation through walls. Unlike existing approaches, TRTAR solely employs a transmissive RIS at the transmitter or receiver without modifying the inherent hardware structure. Experimental results demonstrate that TRTAR attains an average accuracy of 98.13% when signals traverse concrete walls. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_05170 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | TRTAR: Transmissive RIS-assisted Through-the-wall Human Activity Recognition Liu, Junshuo Huang, Yunlong Zhang, Jianan Xiong, Rujing Qiu, Robert Caiming Systems and Control Device-free human activity recognition plays a pivotal role in wireless sensing. However, current systems often fail to accommodate signal transmission through walls or necessitate dedicated noise removal algorithms. To overcome these limitations, we introduce TRTAR: a device-free passive human activity recognition system integrated with a transmissive reconfigurable intelligent surface (RIS). TRTAR eliminates the necessity for dedicated devices or noise removal algorithms, while specifically addressing signal propagation through walls. Unlike existing approaches, TRTAR solely employs a transmissive RIS at the transmitter or receiver without modifying the inherent hardware structure. Experimental results demonstrate that TRTAR attains an average accuracy of 98.13% when signals traverse concrete walls. |
| title | TRTAR: Transmissive RIS-assisted Through-the-wall Human Activity Recognition |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2401.05170 |