WiFi-GEN: High-Resolution Indoor Imaging from WiFi Signals Using Generative AI
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911536101982208 |
|---|---|
| author | Shi, Jianyang Zhang, Bowen Dubey, Amartansh Murch, Ross Jing, Liwen |
| author_facet | Shi, Jianyang Zhang, Bowen Dubey, Amartansh Murch, Ross Jing, Liwen |
| contents | Indoor imaging is a critical task for robotics and internet-ofthings. WiFi as an omnipresent signal is a promising candidate for carrying out passive imaging and synchronizing the up-to-date information to all connected devices. This is the first research work to consider WiFi indoor imaging as a multi-modal image generation task that converts the measured WiFi power into a high-resolution indoor image. Our proposedWiFi-GEN network achieves a shape reconstruction accuracy that is 275% of that achieved by physical model-based inversion methods. Additionally, the Frechet Inception Distance score has been significantly reduced by 82%. To examine the effectiveness of models for this task, the first large-scale dataset is released containing 80,000 pairs of WiFi signal and imaging target. Our model absorbs challenges for the model-based methods including the nonlinearity, ill-posedness and non-certainty into massive parameters of our generative AI network. The network is also designed to best fit measured WiFi signals and the desired imaging output. Code: https://github.com/CNFightingSjy/WiFiGEN |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_04317 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | WiFi-GEN: High-Resolution Indoor Imaging from WiFi Signals Using Generative AI Shi, Jianyang Zhang, Bowen Dubey, Amartansh Murch, Ross Jing, Liwen Computer Vision and Pattern Recognition Computation and Language Indoor imaging is a critical task for robotics and internet-ofthings. WiFi as an omnipresent signal is a promising candidate for carrying out passive imaging and synchronizing the up-to-date information to all connected devices. This is the first research work to consider WiFi indoor imaging as a multi-modal image generation task that converts the measured WiFi power into a high-resolution indoor image. Our proposedWiFi-GEN network achieves a shape reconstruction accuracy that is 275% of that achieved by physical model-based inversion methods. Additionally, the Frechet Inception Distance score has been significantly reduced by 82%. To examine the effectiveness of models for this task, the first large-scale dataset is released containing 80,000 pairs of WiFi signal and imaging target. Our model absorbs challenges for the model-based methods including the nonlinearity, ill-posedness and non-certainty into massive parameters of our generative AI network. The network is also designed to best fit measured WiFi signals and the desired imaging output. Code: https://github.com/CNFightingSjy/WiFiGEN |
| title | WiFi-GEN: High-Resolution Indoor Imaging from WiFi Signals Using Generative AI |
| topic | Computer Vision and Pattern Recognition Computation and Language |
| url | https://arxiv.org/abs/2401.04317 |