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Salt Lake City, UT, United States

Patwari N.,University of Utah | Wilson J.,Xandem Technology, LLC
IEEE Transactions on Information Forensics and Security

A wireless network can use the variance of measured received signal strength (RSS) on the links in a network to infer the locations of people or objects moving in the network deployment area. This paper provides a statistical model for the RSS variance as a function of a person's position with respect to the transmitter (TX) and receiver (RX) locations. We show that the ensemble mean of the RSS variance has an approximately linear relationship with the expected value of total affected power (ETAP), for a range of ETAP. We derive approximate expressions for the ETAP as a function of the person's position, for scattering and reflection, which are tested via simulation. Counterintuitively, we show that reflection, not scattering, causes the RSS variance contours to be shaped similar to Cassini ovals. Results reported in past literature and from a new experiment reported in this paper are shown to be as predicted by the analysis. © 2011 IEEE. Source

Patwari N.,University of Utah | Patwari N.,Xandem Technology, LLC | Brewer L.,University of Utah | Tate Q.,University of Utah | And 2 more authors.
IEEE Journal on Selected Topics in Signal Processing

This paper explores using RSS measurements on the links between commercial wireless devices to locate where a breathing person is located and to estimate their breathing rate, in a home, while the person is sitting, lying down, standing, or sleeping. Prior RSS-based device-free localization methods required calibration measurements to be able to locate stationary people, or did not require calibration but only located people who moved. We collect RSS measurements multiple short (3-7 minute) tests and during a longer 66 minute test, and show the location of the breathing person can be estimated, to within about 2 m average error. We describe a detector that distinguishes between sample times during which a person is moving and sample times during which a person is breathing but otherwise motionless. This detector enables removal of motion interference, i.e., RSS changes due to movements other than a person's breathing, and more accurately estimate a person's breathing rate. Being able to locate and monitor a breathing person, without calibration, is important for applications in search and rescue, health care, and security. © 2013 IEEE. Source

Patwari N.,University of Utah | Wilson J.,Xandem Technology, LLC | Ananthanarayanan S.,USA Mobility | Kasera S.K.,University of Utah | Westenskow D.R.,University of Utah
IEEE Transactions on Mobile Computing

This paper shows experimentally that standard wireless networks which measure received signal strength (RSS) can be used to reliably detect human breathing and estimate the breathing rate, an application we call 'BreathTaking'. We present analysis showing that, as a first order approximation, breathing induces sinusoidal variation in the measured RSS on a link, with amplitude a function of the relative amplitude and phase of the breathing-affected multipath. We show that although an individual link may not reliably detect breathing, the collective spectral content of a network of devices reliably indicates the presence and rate of breathing. We present a maximum likelihood estimator (MLE) of breathing rate, amplitude, and phase, which uses the RSS data from many links simultaneously. We show experimental results which demonstrate that reliable detection and frequency estimation is possible with 30 seconds of data, within 0.07 to 0.42 breaths per minute (bpm) RMS error in several experiments. The experiments also indicate that the use of directional antennas may improve the systems robustness to external motion. © 2002-2012 IEEE. Source

McCracken M.,University of Utah | Patwari N.,University of Utah | Patwari N.,Xandem Technology, LLC
IEEE Transactions on Mobile Computing

Ultra-wideband (UWB) multistatic radar can be used for target detection and tracking in buildings and rooms. Target detection and tracking relies on accurate knowledge of the bistatic delay. Noise, measurement error, and the problem of dense, overlapping multipath signals in the measured UWB channel impulse response (CIR) all contribute to make bistatic delay estimation challenging. It is often assumed that a calibration CIR, that is, a measurement from when no person is present, is easily subtracted from a newly captured CIR. We show this is often not the case. We propose modeling the difference between a current set of CIRs and a set of calibration CIRs as a hidden Markov model (HMM). Multiple experimental deployments are performed to collect CIR data and test the performance of this model and compare its performance to existing methods. Our experimental results show an RMSE of 2.85 ns and 2.76 ns for our HMM-based approach, compared to a thresholding method which, if the ideal threshold is known a priori, achieves 3.28 ns and 4.58 ns. By using the Baum-Welch algorithm, the HMM-based estimator is shown to be very robust to initial parameter settings. Localization performance is also improved using the HMM-based bistatic delay estimates. © 2002-2012 IEEE. Source

Agency: NSF | Branch: Standard Grant | Program: | Phase: | Award Amount: 150.00K | Year: 2012

This Small Business Innovation Research (SBIR) Phase I project will investigate the rapid deployment of wireless networks for the purpose of device-free localization (DFL) in tactical through-building surveillance applications. DFL locates people inside of a building using only simple radio devices deployed on the outside of the building. In a police or military operation, these devices are thrown or launched around the building, and then measure the received signal strength (RSS) between many pairs of devices. Within seconds of deployment, the DFL system shows a map tracking people and objects within the building. Such a system requires rapid deployment techniques and real-time operator-free network configuration. This project will advance the state-of-the-art in self-configuring and adaptive wireless networks. Methods for using reconfigurable antennas to direct the antenna pattern through the building regardless of how a sensor lands will be developed. For operator-free deployment, the network will have devices which self-localize, and learn the statistics of the particular radio channels to be measured. The combined results will show that tactically deployed wireless devices can be used to rapidly obtain intelligence regarding occupants before entering a dangerous building.

The broader impact/commercial potential of this project are significant, as lives are lost every year because law enforcement officers do not know what is happening on the other side of a wall. If successful, this project will enable a product for police/SWAT and military special operations forces (SOF) which will save lives by providing actionable intelligence prior to entering a dangerous building. Existing radar technology for through-wall imaging is too expensive ($100k) for all but the most cost-insensitive applications. We plan a product that, because of its low cost, small size, and ease of use, will be standard equipment in police departments and in SOF teams. We will thus capture a portion of an $78 billion surveillance equipment market, which is growing at a 10-13% annual rate. Development of rapid deployment technologies for wireless networks will benefit a wide range of environmental monitoring and internet-of-things systems.

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