Time-of-Flight-Based Radio Tomography for Device Free Localization

Wang, Jie; Gao, Qinghua; Wang, Hongyu; Yu, Yan; Jin, Minglu

doi:10.1109/twc.2013.040213.121001

Cited by 53 publications

(22 citation statements)

References 35 publications

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“…boring receivers, 7 we have a so-called parametric consensus process. Accordingly, the communication overhead and computational complexity can be significantly reduced.…”

Section: Distributed Processingmentioning

confidence: 99%

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TOA-Based Passive Localization Constructed Over Factor Graphs: A Unified Framework

Yuan

Guo

et al. 2019

IEEE Trans. Commun.

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Passive localization based on time of arrival (TOA) measurements is investigated, where the transmitted signal is reflected by a "passive" target and then received at several distributed receivers. After collecting all measurements at receivers, we can determine the target location. The aim of this paper is to provide a unified factor graph based framework for passive localization in wireless sensor networks based on TOA measurements. Relying on the linearization of range measurements, we construct a Forney-style factor graph model and conceive the corresponding Gaussian message passing algorithm to obtain the target location. It is shown that the factor graph can be readily modified for handling challenging scenarios, such as uncertain receiver positions and link failures. Moreover, a distributed localization method based on consensus-aided operation is proposed for a large scale resource constrained network operating without a fusion center. Furthermore, we derive the Cramér-Rao bound (CRB) to evaluate the performance of the proposed algorithm. Our simulation results verify the efficiency of the proposed unified approach and of its distributed implementation.

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“…boring receivers, 7 we have a so-called parametric consensus process. Accordingly, the communication overhead and computational complexity can be significantly reduced.…”

Section: Distributed Processingmentioning

confidence: 99%

“…Finally, the TOA-based methods measure the time required for the signal to travel from the transmitter to a receiver. Using ultra-wideband (UWB) signaling in TOA measurements is capable of providing highprecision range measurement [7], [8]. In this contribution, we mainly focus our attention on the TOA-based method.…”

Section: Introductionmentioning

confidence: 99%

TOA-Based Passive Localization Constructed Over Factor Graphs: A Unified Framework

Yuan

Guo

et al. 2019

IEEE Trans. Commun.

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“…The imaging vector is used to imaging the monitored region and the brightest spot in the generated image reveals the position of the target. The wireless measurements employed in RTI can be RSS variation [1], RSS variance [2,22], multi-dimensional RSS [23,24] or time-of-flight (TOF) [25,26] measurements. Since the linear model of RTI is ill-conditioned, regularization [1,27] can be utilized to solve the inverse problem.…”

Section: Related Workmentioning

confidence: 99%

Bayesian Device-Free Localization and Tracking in a Binary RF Sensor Network

Wang

Liu

et al. 2017

Sensors

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Received-signal-strength-based (RSS-based) device-free localization (DFL) is a promising technique since it is able to localize the person without attaching any electronic device. This technology requires measuring the RSS of all links in the network constituted by several radio frequency (RF) sensors. It is an energy-intensive task, especially when the RF sensors work in traditional work mode, in which the sensors directly send raw RSS measurements of all links to a base station (BS). The traditional work mode is unfavorable for the power constrained RF sensors because the amount of data delivery increases dramatically as the number of sensors grows. In this paper, we propose a binary work mode in which RF sensors send the link states instead of raw RSS measurements to the BS, which remarkably reduces the amount of data delivery. Moreover, we develop two localization methods for the binary work mode which corresponds to stationary and moving target, respectively. The first localization method is formulated based on grid-based maximum likelihood (GML), which is able to achieve global optimum with low online computational complexity. The second localization method, however, uses particle filter (PF) to track the target when constant snapshots of link stats are available. Real experiments in two different kinds of environments were conducted to evaluate the proposed methods. Experimental results show that the localization and tracking performance under the binary work mode is comparable to the those in traditional work mode while the energy efficiency improves considerably.

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“…Bocca et al [33] use spatial filter to recalibrate the reference RSS and fade level of the channels, so as to reduce the error incurred by the change of environment. Wang et al [34] demonstrate that the elliptical model is also suitable for TOF-based DFL with experimental evaluations. Recently, Hamilton et al [13] propose a novel inverse area elliptical model which defines that the shadowing effect of a target on a wireless link is inversely proportional to the size of the smallest ellipse which contains the target and has the transmitter and receiver as foci.…”

Section: Related Workmentioning

confidence: 99%

Toward Accurate Device-Free Wireless Localization With a Saddle Surface Model

Wang

Gao

Pan

et al. 2016

IEEE Trans. Veh. Technol.

Self Cite

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Device-free wireless localization (DFL) is a technique that could locate a target by analyzing its shadowing effect on wireless links which causes the variation of link measurements, while removing the requirement of equipping the target with any device. It could provide fundamental data for pervasive computing, smart environment, and traffic surveillance applications. The observation model which represents the relationship between wireless link measurement and target location is very important for DFL, since it characterizes the shadowing effect of the target on wireless links, and therefor determines the performance of DFL system. In this paper, inspired by measurement results, we propose a saddle surface (SaS) model to describe the shadowing effect. SaS model characterizes the elaborate information within the spatial impact area and provides more useful observation information for the location estimation algorithm. We incorporate the SaS model into the particle filter framework for location estimation. Extensive experiments in indoor and outdoor scenarios are carried out to evaluate the performance of the proposed schemes. The tracking errors of 0.78m and 0.21m in the above two scenarios demonstrate the better performance of the proposed SaS model compared with existing models.

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Time-of-Flight-Based Radio Tomography for Device Free Localization

Cited by 53 publications

References 35 publications

TOA-Based Passive Localization Constructed Over Factor Graphs: A Unified Framework

TOA-Based Passive Localization Constructed Over Factor Graphs: A Unified Framework

Bayesian Device-Free Localization and Tracking in a Binary RF Sensor Network

Toward Accurate Device-Free Wireless Localization With a Saddle Surface Model

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