Device-free and device-bound activity recognition using radio signal strength
Proceedings of the 4th Augmented Human International Conference
SCPL: indoor device-free multi-subject counting and localization using radio signal strength
Proceedings of the 12th international conference on Information processing in sensor networks
Radio tomographic imaging and tracking of stationary and moving people via kernel distance
Proceedings of the 12th international conference on Information processing in sensor networks
Demo abstract: a radio tomographic system for real-time multiple people tracking
Proceedings of the 12th international conference on Information processing in sensor networks
Device-free people counting and localization
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
From RSSI to CSI: Indoor localization via channel response
ACM Computing Surveys (CSUR)
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Device-free localization (DFL) is the estimation of the position of a person or object that does not carry any electronic device or tag. Existing model-based methods for DFL from RSS measurements are unable to locate stationary people in heavily obstructed environments. This paper introduces measurement-based statistical models that can be used to estimate the locations of both moving and stationary people using received signal strength (RSS) measurements in wireless networks. A key observation is that the statistics of RSS during human motion are strongly dependent on the RSS "fade level” during no motion. We define fade level and demonstrate, using extensive experimental data, that changes in signal strength measurements due to human motion can be modeled by the skew-Laplace distribution, with parameters dependent on the position of person and the fade level. Using the fade-level skew-Laplace model, we apply a particle filter to experimentally estimate the location of moving and stationary people in very different environments without changing the model parameters. We also show the ability to track more than one person with the model.