Feasibility and limits of wi-fi imaging
Donny Huang,Rajalakshmi Nandakumar,Shyamnath Gollakota +2 more
- pp 266-279
TLDR
This work implements a prototype wireless receiver using USRP-N210s at 2.4 GHz and demonstrates that it can image objects such as leather couches and metallic shapes in line-of-sight and non-line ofsight scenarios and can localize static human subjects and metallic objects with a median accuracy of 26 and 15 cm respectively.Abstract:
We explore the feasibility of achieving computational imaging using Wi-Fi signals. To achieve this, we leverage multi-path propagation that results in wireless signals bouncing off of objects before arriving at the receiver. These reflections effectively light up the objects, which we use to perform imaging. Our algorithms separate the multi-path reflections from different objects into an image. They can also extract depth information where objects in the same direction, but at different distances to the receiver, can be identified. We implement a prototype wireless receiver using USRP-N210s at 2.4 GHz and demonstrate that it can image objects such as leather couches and metallic shapes in line-of-sight and non-line-of-sight scenarios. We also demonstrate proof-of-concept applications including localization of static humans and objects, without the need for tagging them with RF devices. Our results show that we can localize static human subjects and metallic objects with a median accuracy of 26 and 15 cm respectively. Finally, we discuss the limits of our Wi-Fi based approach to imaging.read more
Citations
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Understanding and Modeling of WiFi Signal Based Human Activity Recognition
TL;DR: CARM is a CSI based human Activity Recognition and Monitoring system that quantitatively builds the correlation between CSI value dynamics and a specific human activity and recognizes a given activity by matching it to the best-fit profile.
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Gait recognition using wifi signals
TL;DR: The intuition is that due to the differences in gaits of different people, the WiFi signal reflected by a walking human generates unique variations in the Channel State Information on the WiFi receiver, so WifiU is proposed, which uses commercial WiFi devices to capture fine-grained gait patterns to recognize humans.
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WiFi Sensing with Channel State Information: A Survey
TL;DR: This survey gives a comprehensive review of the signal processing techniques, algorithms, applications, and performance results of WiFi sensing with CSI, and presents three future WiFi sensing trends, i.e., integrating cross-layer network information, multi-device cooperation, and fusion of different sensors for enhancing existing WiFi sensing capabilities and enabling new WiFi sensing opportunities.
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Millimeter Wave Communication: A Comprehensive Survey
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Petr Beckmann,Andre Spizzichino +1 more
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Proceedings ArticleDOI
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