An Enhanced Geometric Filter Algorithm With Channel Diversity for Device-Free Localization

Talampas, Marc Caesar R.; Low, Kay Soon

doi:10.1109/tim.2015.2490818

Cited by 22 publications

(16 citation statements)

References 23 publications

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“…Based on the above analysis, the geometric-based part of the SDFL algorithm includes the following steps: effective links identification, removing outlier links via spatial prior information, and generating a position estimate. Unlike the earlier work, 29,30,32 we adopt the nonweighted mean method to estimate the target's position for avoiding the difficulty of acquiring the reasonable weights. Moreover, the spatial prior region is modified in ''Removing outlier links via spatial prior information'' subsection.…”

Section: Gdfl Methodsmentioning

confidence: 99%

“…Although the last step of GDFL alleviates the negative effect of noise, the RSS variations in some wireless links that are not near the target may be also above the threshold value y th because of multipath effects. 27,28 As shown in the work of Talampas and Low, 29,30 outlier links may intersect with other effective links at points far away from the target, so outlier links that are not excluded from the localization process will result in large tracking errors. To remove the negative effect of such outlier links on the target position estimate, there are two kinds of approach to distinguish the target-affected links and the outlier links.…”

Section: Gdfl Methodsmentioning

confidence: 99%

“…Furthermore, an enhanced GF algorithm was proposed by exploiting multichannel RSS measurements to enhance the DFL accuracy in cluttered environments. 30 The GF method neither needs to solve the underdetermined problem in model-based DFL nor needs to store the offline fingerprint database, so these characteristics reduce the algorithm's storage and computational load. However, the weighted mean of all possible intersection points of shadowed links is a coarse approximation.…”

Section: Related Workmentioning

confidence: 99%

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A two-step sequential method for device-free localization using wireless sensor networks

Wang

Hua

2019

International Journal of Distributed Sensor Networks

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The device-free localization that locates an uncooperative target by analyzing its shadowing effect on wireless links in low-cost wireless sensor networks has become an attractive technique. Due to its ability of realizing localization without the need of equipping the target with any wireless device, device-free localization has lots of potential applications ranging from emergency rescue to patient monitoring. However, since the change of the received signal strength on links easily becomes unpredictable due to all kinds of dynamical factors, device-free localization is still a challenging problem. In this article, we propose a novel two-step sequential device-free localization method to enhance device-free localization performance. The sequential device-free localization first utilizes the geometric-based method to quickly obtain the coarse positioning result with low storage and computational requirements, and then exploits the location information of selected wireless nodes to improve the accuracy of the geometric-based localization result based on the optimization-based method, which ensures its robust performance. Experimental results in indoor and outdoor scenarios demonstrate the effectiveness and robustness of the proposed scheme.

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Section: Gdfl Methodsmentioning

confidence: 99%

Section: Gdfl Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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A two-step sequential method for device-free localization using wireless sensor networks

Wang

Hua

2019

International Journal of Distributed Sensor Networks

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“…Talampas 32 , et al extended their research in an enhanced multichannel GF algorithm that improves accuracy in cluttered environment. The algorithm used channel differences to impede the effects of multipath environment on the network's links.…”

Section: Techniques Based On Statistical Methodsmentioning

confidence: 99%

Device Free Localisation Techniques in Indoor Environments

2019

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The location estimation of a target for a long period was performed only by device based localisation technique which is difficult in applications where target especially human is non-cooperative. A target was detected by equipping a device using global positioning systems, radio frequency systems, ultrasonic frequency systems, etc. Device free localisation (DFL) is an upcoming technology in automated localisation in which target need not equip any device for identifying its position by the user. For achieving this objective, the wireless sensor network is a better choice due to its growing popularity. This paper describes the possible categorisation of recently developed DFL techniques using wireless sensor network. The scope of each category of techniques is analysed by comparing their potential benefits and drawbacks. Finally, future scope and research directions in this field are also summarised. 6. Ramesh, M. V. Design, development, and deployment of a wireless sensor network for detection of landslides. Ad

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“…wireless sensors and sensor networks were effectively used in the implementation of solutions belonging to numerous fields, along with environmental monitoring [9]- [11], natural disaster prevention [12], modern consumption monitoring in big homes [13], monitoring systems for the symmetry of radiology operators in healthcare programs [14], [15], area tracking of humans, belongings or risky gases [16], [17], tools circumstance surveillance [18] and procedure manage [19] in business environments, and lots of others. The maximum widely used protocols in packages having less tight latency and reliability necessities, along with environmental monitoring, consist of zigbee (IEEE 802.15.4) [6], Wi-Fi (based on IEEE 802.11) , and Bluetooth (IEEE 802.15.1) .…”

Section: Introductionmentioning

confidence: 99%

Review of IoT-Based Wireless Sensors for Environmental Monitoring

2017

IJSR

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Abstract:The recent adjustments in climate have multiplied the significance of environmental tracking, making it a topical and surprisingly active studies region. This Wi-Fi ld is based on remote sensing and on wireless sensor networks for gathering statistics about the environment. Current improvements, such as the vision of the internet of things (IoT), the cloud computing model, and cyber-physical structures, offer aid for the transmission and control of big amounts of information concerning the trends determined in environmental parameters.In this context, the present day paintings offers three specific IoT-primarily based Wi-Fi sensors for environmental and ambient tracking: one using user datagram protocol (UDP)-based totally wireless verbal exchange, one communicating thru Wi-Fi and hypertext transfer protocol (HTTP), and a 1/3 one using Bluetooth clever. All the offered systems provide the possibility of recording facts at far flung places and of visualizing them from each device with an internet connection, allowing the tracking of geographically massive regions. The improvement details of those systems are described, along with the predominant variations and similarities among them. the feasibility of the 3 developed structures for implementing monitoring packages, considering their power autonomy, ease of use, answer complexity, and net connectivity facility, turned into analyzed, and revealed that they make excellent applicants for IoT-primarily based solutions.

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An Enhanced Geometric Filter Algorithm With Channel Diversity for Device-Free Localization

Cited by 22 publications

References 23 publications

A two-step sequential method for device-free localization using wireless sensor networks

A two-step sequential method for device-free localization using wireless sensor networks

Device Free Localisation Techniques in Indoor Environments

Review of IoT-Based Wireless Sensors for Environmental Monitoring

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