IEEE Transactions on Mobile Computing · 2011 · 194 citations · 16 references
Wireless CommunicationsLocation TrackingEngineeringLocation EstimationPositioning SystemDevice-free LocalizationLocalization TechniqueSignal Strength MeasurementsLocalizationWireless LocalizationKinesiologyLocation AwarenessHuman MotionWireless SystemsHealth SciencesComputer EngineeringWireless NetworkingMobile ComputingRf LocalizationSignal ProcessingWireless NetworksWireless PropagationIndoor Positioning SystemReceived Signal Strength
Device‑free localization estimates the position of a person or object without a tag, but existing model‑based RSS methods cannot locate stationary people in heavily obstructed environments. This paper proposes measurement‑based statistical models that enable estimation of both moving and stationary people’s locations from RSS measurements in wireless networks. The authors observe that RSS statistics during motion depend on the fade level during no motion, and use a fade‑level skew‑Laplace model within a particle filter to estimate locations across diverse environments without retuning parameters. Experimental data show that motion‑induced signal changes follow a skew‑Laplace distribution whose parameters vary with person position and fade level, and the model can track multiple people simultaneously.
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.
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Monte Carlo Statistical Methods
Hoon Kim, Christian P. Robert, George Casella · Technometrics · 2000 · 5.6K citations
Rudolph van der Merwe, Nando de Freitas, Eric A. Wan · 2000 · 1.4K citations