Radio Tomographic Imaging with Wireless Networks

By Joey Wilson, Neal Patwari. IEEE Transactions on Mobile Computing, 2010

920 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/3b74f7cfe02477b02365b42355773866737a0dc4

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In Biomedical Engineering

In Electrical and Electronic Engineering

Citations in the corpus

Non-Invasive Detection of Moving and Stationary Human With WiFi (2015, methodology): “The primary underpinning is that a person’s movement C. Wu, Z. Yang and Y. Liu are with the School of Software and TNList, Tsinghua University.”
A Survey on Behavior Recognition Using WiFi Channel State Information (2017, methodology): “[6] J.”
RF Sensor Networks for Device-Free Localization: Measurements, Models, and Algorithms (2010, methodology): “Results have been presented which co unt the number of people moving [9], estimate a person’s locatio n [18], [13], [17], [14], and image the movements in an area of interest [11], [12], [13], [15], all in real-world multip ath environments.”
CoSDEO 2016 Keynote: A decade later — Challenges: Device-free passive localization for wireless environments (2016, background): “ing techniques [5], [6], multi-persons tracking systems [7]–”
We Can Hear You with Wi-Fi! (2014, background): “WiHear builds upon recent research that leverages radio reflections from human bodies to detect, track, and recognize motions [35].”
Comparing Ubisense, BeSpoon, and DecaWave UWB Location Systems: Indoor Performance Analysis (2017, background): “This approach does not require a person to carry any device, so are commonly known as device-free localization solutions [12]–[14].”
Dynamic-MUSIC: accurate device-free indoor localization (2016, methodology): “Existing solutions either require high density deployment [22, 42] or need dedicated devices to send out customized signals [18, 42], limiting large scale deployments in real life.”
Whole-home gesture recognition using wireless signals (2013, background): “WiSee is also related to work on wireless tomography that aims to localize humans by deploying a network of sensors throughout the environment [26, 24, 23].”
Multi-Person Localization via RF Body Reflections (2015, background): “0 neither requires deploying a network of dozens to hundreds of sensors [36, 18] nor does it require extensive calibration [25, 24, 40].”
A Survey on Wireless Indoor Localization from the Device Perspective (2016, methodology): “In Wilson and Patwari [2010], a radio tomographic imaging (RTI) technique is presented for imaging the passive moving entities by applying a linear model. Wilson and Patwari [2011] make an improvement by leveraging motion-induced variance of RSS measurements.”
3D Tracking via Body Radio Reflections (2014, background): “Some past works in radio tomography use a network of tens or hundred sensors to track a person even if she does not carry any wireless device [29, 30].”
Wireless Sensing for Human Activity: A Survey (2020, methodology): “Wilson and Patwari also proposed vRTI [20], an extension of the RTI technique, by leveraging the motion-induced variance of RSS measurements for better activity recognition.”
E-eyes: device-free location-oriented activity identification using fine-grained WiFi signatures (2014, background): “The radio tomography imaging (RTI) [35, 45] and device-free passive (DfP) localization [19,29,39,44], like our system, do not require a device be attached to or carried by the user.”
See-Through Walls: Motion Tracking Using Variance-Based Radio Tomography Networks (2011, background): “Previous work shows that changes in link path losses can be used to accurately estimate an image of the attenuation field, that is, a spatial plot of attenuation per unit area [1].”
Nuzzer: A Large-Scale Device-Free Passive Localization System for Wireless Environments (2009, background): “Another emerging technology is Radio Tomographic Imaging (RTI) [6].”