Peter Hillyard

You're crossing the line: Localizing border crossings using wireless RF links

Detecting and localizing a person crossing a line segment, i.e., border, is valuable information in security systems and human context awareness. To that end, we propose a border crossing localization system that uses the changes in measured received signal strength (RSS) on links between transceivers deployed linearly along the border. Any single link has a low signal-to-noise ratio because its RSS also varies due to environmental change, (e.g., branches swaying in wind), and sometimes does not change significantly when a person crosses it. The redundant, overlapping nature of the links between many possible pairs of nodes in the network provides an opportunity to mitigate errors. We propose new classifiers to use the redundancy to estimate where a person crosses the border. Specifically, the solution of these classifiers indicates which pair of neighboring nodes the person crosses between. We demonstrate that in many cases, these classifiers provide more robust border crossing localization compared to a classifier that excludes these noisy, redundant measurements.

Highly reliable signal strength-based boundary crossing localization in outdoor time-varying environments

Detecting and locating outdoor border crossing events is valuable information in curbing drug trafficking, reducing poaching, and protecting high-asset equipment and goods. However, border sensing is notoriously challenging, prone to false alarms and missed detections, with serious consequences. Weather events, like rain and wind, make it even more challenging to maintain a low level of missed detections and false alarms. In this paper, we propose and test an automated system of wireless sensors which uses received signal strength (RSS) measurements to localize where a border crossing occurs. In addition, we develop new RSS-based statistical models and methods that can quickly be initialized and updated by using link RSS statistics to adapt to time-varying RSS changes due to weather events. These models are implemented in two new classifiers that localize border crossings with few missed detections and false alarms. We validate our proposed methods by implementing one of the classifiers in a three month long deployment of a solar-powered, real-time system that captures images of the border for ground truth validation. Furthermore, over 75 hours of RSS measurements are collected with an emphasis on collection during weather events, like rain and wind, during which we expect our classifiers to perform the worst. We demonstrate that the proposed classifiers outperform four other baseline classifiers in terms of false alarm probability by 1 to 4 orders of magnitude, and in terms of the misclassification probability by 1 to 2 orders of magnitude.

Experience: Cross-Technology Radio Respiratory Monitoring Performance Study

This paper addresses the performance of systems which use commercial wireless devices to make bistatic RF channel measurements for non-contact respiration sensing. Published research has typically presented results from short controlled experiments on one system. In this paper, we deploy an extensive real-world comparative human subject study. We observe twenty patients during their overnight sleep (a total of 160 hours), during which contact sensors record ground-truth breathing data, patient position is recorded, and four different RF breathing monitoring systems simultaneously record measurements. We evaluate published methods and algorithms. We find that WiFi channel state information measurements provide the most robust respiratory rate estimates of the four RF systems tested. However, all four RF systems have periods during which RF-based breathing estimates are not reliable.

Focusing through walls: An E-shaped patch antenna improves whole-home radio tomography

Tagless identification and tracking with through-wall received signal strength-based radio tomographic imaging (RTI) allows emergency responders to learn where people are inside of a building before entering the building. Use of directional antennas in RTI nodes focuses RF power along the link line, improving system performance. However, antennas placed on a buildings exterior wall can be detuned by their close proximity to the dielectric, thus sending power across wider angles and resulting in less accurate imaging. In this paper, we improve through-wall RTI by using an E-shaped patch antenna we design to be mounted to an exterior wall. Along with its directionality, the E-shaped patch antenna is designed to avoid impedance mismatches when brought into close proximity of a dielectric material, thus increasing radiation through the exterior wall and along the link line. From our experiments, we demonstrate that the E-shaped patch antenna can reduce the median root mean square localization error by up to 43% when compared to microstrip patch and omnidirectional antennas. For equal error performance, the E-shaped patch antenna allows an RTI system to reduce power and bandwidth usage by using fewer nodes and measuring on fewer channels.

Poster: Link Line Crossing Speed Estimation with Narrowband Signal Strength

We present results from a system which uses received signal strength (RSS) measurements to estimate the speed at which a person is walking when they cross the link line. While many RSS-based device-free localization systems can detect a line crossing, this system estimates additionally the speed of crossing, which can provide significant additional information to a tracking system. Further, unlike device-free RF sensors which occupy tens of MHz of bandwidth, this system uses a channel of about 10 kHz. Experiments with a person walking from 0.3 to 1.8 m/s show the system can measure walking speed within 0.05 m/s RMS error.

On-Wall, Wide Bandwidth E-Shaped Patch Antenna for Improved Whole-Home Radio Tomography

Tagless identification and tracking with through-wall received signal strength-based radio tomographic imaging (RTI) allows emergency responders to learn where people are inside of a building before entering the building. Use of directional antennas in RTI nodes focuses RF power along the link line, improving system performance. However, antennas placed on a building’s exterior wall can be detuned by their close proximity to the dielectric, thus sending power across wider angles and resulting in less accurate imaging. In this paper, we improve through-wall RTI by using an E-shaped patch antenna we design to be mounted to an exterior wall. Along with its directionality, the E-shaped patch antenna is designed to avoid impedance mismatches when brought into close proximity of a dielectric material, thus increasing radiation through the exterior wall and along the link line. From our experiments, we demonstrate that the E-shaped patch antenna can reduce the median root mean square localization error by up to 43% when compared to microstrip patch and dipole antennas. For equal error performance, the E-shaped patch antenna allows an RTI system to reduce power and bandwidth usage by using fewer nodes and measuring on fewer channels.

Detecting and localizing border crossings using RF links

Detecting and localizing a person crossing a line segment, i.e., border, is valuable information in security and data analytic applications. To that end, we use the received signal strength (RSS) measured on RF links between nodes deployed linearly along a border as a border crossing detection and localization system. RSS measurements from any single RF link are noisy and prone to variations due to environmental changes (e.g. branches moving in wind). The redundant overlapping nature of the links between pairs of nodes in our proposed system provides an opportunity to mitigate these issues. We propose a hidden Markov model (HMM) which models the RSS on network links as a function of the neighboring nodes between which a person crosses. We demonstrate that the forward-backward solution to this HMM provides a robust and real time border crossing detection and localization system.

Track estimation using link line crossing information in wireless networks

Device-free or non-cooperative localization uses the changes in signal strength measured on links in a wireless network to estimate a persons position in the network area. Existing methods provide an instantaneous coordinate estimate via radio tomographic imaging or location fingerprinting. In this paper, we explore the problem of, after a person has exited the area of the network, how can we estimate their path through the area? We present two methods which use recent line crossings detected by the networks links to estimate the persons path through the area. We assume that the person took a linear path and estimate the paths parameters. One method formulates path estimation as a line stabbing problem, and another method is a linear regression formulation. Through simulation we show that the line stabbing approach is more robust to false detections, but in the absence of false detections, the linear regression method provides superior performance.