See-Through Walls: Motion Tracking Using Variance-Based Radio Tomography Networks

By Joey Wilson, Neal Patwari. IEEE Transactions on Mobile Computing, 2011

520 citations recorded by the source corpus.

This paper presents a new method for imaging, localizing, and tracking motion behind walls in real time. The method takes advantage of the motion-induced variance of received signal strength measurements made in a wireless peer-to-peer network. Using a multipath channel model, we show that the signal strength on a wireless link is largely dependent on the power contained in multipath components that travel through space containing moving objects. A statistical model relating variance to spatial locations of movement is presented and used as a framework for the estimation of a motion image. From the motion image, the Kalman filter is applied to recursively track the coordinates of a moving target. Experimental results for a 34-node through-wall imaging and tracking system over a 780 square foot area are presented.

Subfields: Biomedical Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/422b68115ce86e87b8c54600f21631588fc4711d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Zimu Zhou
2. Dina Katabi
3. Neal Patwari
4. Joey Wilson
5. Jiannong Cao
6. Xuefeng Liu
7. Hongbo Liu
8. Jian Liu

In Electrical and Electronic Engineering

1. Zheng Yang
2. Yunhao Liu
3. Chenshu Wu
4. Zimu Zhou
5. Zachary Kabelac
6. Joey Wilson
7. Fadel M. Adib
8. Jiannong Cao

Citations in the corpus

• Non-Invasive Detection of Moving and Stationary Human With WiFi (2015, background): “Numerous research has explored to detect human movements non-invasively for localization and tracking [1], [3], counting [2], [26] or activity recognition [17].”
• Wi-Sleep: Contactless Sleep Monitoring via WiFi Signals (2014, methodology): “In [12], the RSSI variance caused by moving objects is utilized to track the location of a person or object behind walls.”
• 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].”
• Capturing the human figure through a wall (2015, background): “…them to carry a wireless transmitter, e.g., [Adib et al. 2014; Joshi et al. 2015; Adib and Katabi 2013; Wang et al. 2014b; Pu et al. 2013; Abdelnasser et al. 2015; Chetty et al. 2012; Wilson and Patwari 2011; Youssef et al. 2007; Nan-nuru et al. 2013; Seifeldin et al. 2013; Adib et al. 2015].”
• Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks (2019, unknown): “raphy, Tikhonov least-squares regularization, and Kalman tracking [367].”
• 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.”
• RF Sensor Networks for Device-Free Localization: Measurements, Models, and Algorithms (2010, methodology): “Analytical results in [13] suggest that the change in variance can be detected even when the power in the affected multipath is 10 dB less than the multipaths which do not go through the building.”