Challenges: device-free passive localization for wireless environments

By Moustafa Youssef, Matthew Mah, Ashok K. Agrawala. ACM/IEEE International Conference on Mobile Computing and Networking, 2007

399 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/ddb0baa3925852738624f405a697f4ff1608bb5f

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Citations in the corpus

LiFS: low human-effort, device-free localization with fine-grained subcarrier information (2016, background): “This problem is still an open issue [48, 46] and we leave it as the future work.”
A survey of active and passive indoor localisation systems (2012, methodology): “[70] affirms the challenging aspect of the UWB radar is the data acquisition due to the high sampling rate required by the Nyquist's sampling theorem, a rate of over 2 GS/s for 1 GHz bandwidth.”
CoSDEO 2016 Keynote: A decade later — Challenges: Device-free passive localization for wireless environments (2016, background): “the concept of device-free passive localization [4], in which”
See-Through Walls: Motion Tracking Using Variance-Based Radio Tomography Networks (2011, background): “Youssef, Mah, and Agrawala [2] demonstrated that variance of RSS on a number of WiFi links in an indoor WLAN can be used to 1) detect if motion is occurring within a wireless network and 2) localize the moving object based on a manually trained lookup.”
Radio Tomographic Imaging with Wireless Networks (2010, background): “Experiments in [14] demonstrate the capability of a detector based on signal strength measurements determine the location of a person who is not carrying an electronic device.”
A Survey on Wireless Indoor Localization from the Device Perspective (2016, background): “WiFi-based approaches [Youssef et al. 2007], on the other hand, leverage the available infrastructure for device-free indoor localization.”
Capturing the human figure through a wall (2015, background): “…them to carry a wireless transmitter, e.g., [Adib et al. 2014; Joshi et al. 2015; Adib and Katabi 2013; Wang et al. 2014b; Pu et al. 2013; Abdelnasser et al. 2015; Chetty et al. 2012; Wilson and Patwari 2011; Youssef et al. 2007; Nan-nuru et al. 2013; Seifeldin et al. 2013; Adib et al. 2015].”
Dynamic-MUSIC: accurate device-free indoor localization (2016, background): “The state-of-the-art device-free approaches on commodity Wi-Fi devices employ RSSI or CSI as ﬁngerprints [1, 30, 53], which need substantial radio-map survey and labor-intensive ofﬂine training.”
Whole-home gesture recognition using wireless signals (2013, background): “There has also been recent interest in device-free localization of humans [28, 20] using the RSSI information from wire-”
Location, Localization, and Localizability (2010, background): “To tackle this issue, recently a novel concept of Device-Free Localization, also called Transceiver-Free Localization, is proposed[57-58].”
Nuzzer: A Large-Scale Device-Free Passive Localization System for Wireless Environments (2009, methodology): “Recently, we proposed the device-free passive localization ( DfP ) concept [5].”
3D Tracking via Body Radio Reflections (2014, background): “Other works on device-free localization rely on RSSI ﬁngerprints [32, 24], which are generated in a training phase by asking a person to stand in different locations throughout the area of interest.”
E-eyes: device-free location-oriented activity identification using fine-grained WiFi signatures (2014, background): “More generally, some activities that are tied to particular places can also be inferred from location systems [7, 44], either device-based or device-free, but these system cannot distinguish multiple activities that occur in the same place.”
Accurate Passive Location Estimation Using TOA Measurements (2012, methodology): “However, both the DfP and RFID methods are based on preliminary environmental measurement information like " Radio Map Construction " [19] and " fingerprints " [20].”
Multi-Person Localization via RF Body Reflections (2015, background): “0 neither requires deploying a network of dozens to hundreds of sensors [36, 18] nor does it require extensive calibration [25, 24, 40].”
RF Sensor Networks for Device-Free Localization: Measurements, Models, and Algorithms (2010, methodology): “Finally, estimation of a histogram requires severa l measurements, for example 26 consecutive RSS measurements in [17], or 60 seconds in [1], while a person is standing in one location.”