An RF-Based System for Tracking Transceiver-Free Objects

Zhang, Dian; Ma, Jian; Quan-bin, Chen; Ni, Lionel M.

doi:10.1109/percom.2007.8

Cited by 234 publications

(212 citation statements)

References 14 publications

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“…One of well-known techniques to detect (further track) some device-free object(s) is to use RSSI or LQI since the value of RSSI and LQI of a link connecing two wireless sensor nodes will be affected by the physical situation between these two nodes [15], [21]. However, RSSI and LQI of a wireless link depend on many aspects, like weather, temperature, hardware constraints and so on [6].…”

Section: B Finding Effective Detection Methodsmentioning

confidence: 99%

“…However, the work in [10], [17] assume that every sensor node has a sensing range and can detect the existence of an object accurately as long as the object falls into the sensing range of this sensor node, which is not realistic. Later, Zhang et al [21], [22] and Yao et al, [19] proposed to use RF-based method to track transceiver-free objects. Their main idea is to detect objects based on SmallScale Fading effect (SSF).…”

Section: Related Workmentioning

confidence: 99%

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Multiple Objects Device-Free Passive Tracking Using Wireless Sensor Networks

Mao

Tang

2011

2011 IEEE International Conference on Communications (ICC)

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Abstract-object tracking is a main application of wireless sensor networks (WSNs), and has been studied widely [4], [9]. In this work, we study multiple objects tracking problem using WSNs, in which we assume no equipment is carried by the object and the tracking procedure is passive. We first show that without carefully design, Received Signal Strength Indicator (RSSI) and Link Quality Indicator (LQI) are not as effective as claimed for passive detection through our testbed studies. We further propose to use light to track moving objects in WSNs. To our best knowledge, this is the first work to study multiple objects tracking using light sensors and general light sources. We propose a novel probabilistic tracking protocol to track multiple objects. We further design several efficient methods to compute some attributes of the moving objects (like heights, moving speeds etc.).

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Section: B Finding Effective Detection Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Multiple Objects Device-Free Passive Tracking Using Wireless Sensor Networks

Mao

Tang

2011

2011 IEEE International Conference on Communications (ICC)

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“…The early exploration of DFPL was performed independently and almost synchronously by Youssef et al [4,5] and Zhang et al [6,7]. Zhang et al [6,7] presented a geometric method, and adopted the dynamic cluster-based algorithm to solve the DFPL problem. They also proposed a real-time and scalable system for realizing DFPL, where they divided the tracking field into areas and used the support vector regression model to locate the target in each area.…”

Section: Related Workmentioning

confidence: 99%

“…The shadowed links will be different when the target is locate at different locations, and this makes it possible to realize DFPL based on the link measurements [3][4][5][6][7][8][9]. Different from that the line-of-sight (LOS) path is dominant in an open outdoor environment, multipath is common in an indoor environment, and thus the change in RSS due to target presence becomes unpredictable.…”

Section: Introductionmentioning

confidence: 99%

Device-Free Electromagnetic Passive Localization With Frequency Diversity

Ke¹,

Yuan

Zhang³

et al. 2016

PIER M

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Abstract-As an emerging wireless localization technique, the electromagnetic passive localization without the need of carrying any device, named device-free passive localization (DFPL) technique has drawn considerable research attention. The DFPL technique detects shadowed links in the monitored area and realizes localization with the received signal strength (RSS) measurements of these links. However, the current RSS-based DFPL techniques have two major challenges: one is that the RSS signal is particularly sensitive to noise, and the other is that it needs a sufficient number of nodes to provide enough RSS measurements of wireless links to guarantee good performance. To overcome these problems, in this paper we take advantage of compressive sensing (CS) theory to handle the spatial sparsity of the DFPL problem for reducing the number of nodes required by DFPL systems and exploit the frequency diversity technique to deal with the problem of the RSS sensitivity. Meanwhile, inspired by the fact that the target's movement is continuous and that the target's current location must be around the last location, we add prior information on the support region into the sparse reconstruction process for enhancing sparse reconstruction performance. The effectiveness and robustness of the proposed scheme are demonstrated by experimental results where the proposed algorithm yields substantial improvement for localization performance.

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“…Signature-based algorithms can be effective but require extensive training data to learn signal patterns that correspond to target positions. Radio-frequency tomographic imaging/tracking avoids this by proposing a statistical model for the change induced in the mean (or variance) of the received signal strength of a transmission when a target is present between the wireless transmitter and receiver [2,3,4,5].…”

Section: Introductionmentioning

confidence: 99%

Sequential Monte Carlo Radio-Frequency tomographic tracking

Chen

Coates

et al. 2011

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Radio Frequency (RF) tomographic tracking is the process of tracking moving targets by analyzing changes of attenuation in wireless transmissions. This paper presents a novel sequential Monte Carlo (SMC) method for RF tomographic tracking of a single target using a wireless sensor network. The algorithm incorporates on-line Expectation Maximization (EM) to estimate model parameters. Based on experimental measurements, we introduce a new measurement model for the attenuation caused by a target. We assess performance through numerical simulation and demonstrate that it significantly outperforms previous RF tomographic tracking procedures.

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An RF-Based System for Tracking Transceiver-Free Objects

Cited by 234 publications

References 14 publications

Multiple Objects Device-Free Passive Tracking Using Wireless Sensor Networks

Multiple Objects Device-Free Passive Tracking Using Wireless Sensor Networks

Device-Free Electromagnetic Passive Localization With Frequency Diversity

Sequential Monte Carlo Radio-Frequency tomographic tracking

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