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Robust Estimators for Variance-Based Device-Free Localization and Tracking
Yang Zhao,Neal Patwari +1 more
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In this paper, two estimators were proposed to reduce the impact of the variations caused by intrinsic motion in a DFL system, such as branches moving in the wind and rotating or vibrating machinery.Abstract:
Human motion in the vicinity of a wireless link causes variations in the link received signal strength (RSS). Device-free localization (DFL) systems, such as variance-based radio tomographic imaging (VRTI), use these RSS variations in a static wireless network to detect, locate and track people in the area of the network, even through walls. However, intrinsic motion, such as branches moving in the wind and rotating or vibrating machinery, also causes RSS variations which degrade the performance of a DFL system. In this paper, we propose and evaluate two estimators to reduce the impact of the variations caused by intrinsic motion. One estimator uses subspace decomposition, and the other estimator uses a least squares formulation. Experimental results show that both estimators reduce localization root mean squared error by about 40% compared to VRTI. In addition, the Kalman filter tracking results from both estimators have 97% of errors less than 1.3 m, more than 60% improvement compared to tracking results from VRTI.read more
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Whole-home gesture recognition using wireless signals
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Device-Free Wireless Localization and Activity Recognition: A Deep Learning Approach
TL;DR: A sparse autoencoder network is designed to automatically learn discriminative features from the wireless signals and merge the learned features into a softmax-regression-based machine learning framework to realize location, activity, and gesture recognition simultaneously.
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CSI-Based Device-Free Wireless Localization and Activity Recognition Using Radio Image Features
TL;DR: A radio image processing approach is explored and exploited to better characterize the influence of human behaviors on Wi-Fi signals and transform CSI measurements from multiple channels into a radio image, extract color and texture features from the radio image and adopt a deep learning network to learn optimized deep features from image features.
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Feasibility and limits of wi-fi imaging
TL;DR: This work implements a prototype wireless receiver using USRP-N210s at 2.4 GHz and demonstrates that it can image objects such as leather couches and metallic shapes in line-of-sight and non-line ofsight scenarios and can localize static human subjects and metallic objects with a median accuracy of 26 and 15 cm respectively.
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Radio tomographic imaging and tracking of stationary and moving people via kernel distance
TL;DR: This work presents and evaluates a system which can locate stationary or moving people, without calibration, by using kernel distance to quantify the difference between two histograms of signal strength measurements.
References
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