Vehicle Tracking Using Particle Filter in Wi-Fi Network

Lu, Henghui; Zhang, Sheng; Liu, Xingchuan; Lin, Xiao

doi:10.1109/vetecf.2010.5594099

Cited by 9 publications

(5 citation statements)

References 10 publications

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“…The majority of work in this area, such as RADAR fingerprinting [6], outdoor Wi-Fi localization [10,17] and sensor network localization [9,27,12,20,7] relies on the localized device being an active participant in the process.…”

Section: Introductionmentioning

confidence: 99%

Tracking unmodified smartphones using wi-fi monitors

Musa

Eriksson

2012

Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems

219

176

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Smartphones with Wi-Fi enabled periodically transmit Wi-Fi messages, even when not associated to a network. In one 12-hour trial on a busy road (average daily traffic count 37,000 according to the state DOT), 7,000 unique devices were detected by a single road-side monitoring station, or about 1 device for every 5 vehicles.In this paper, we describe a system for passively tracking unmodified smartphones, based on such Wi-Fi detections. This system uses only common, off-the-shelf access point hardware to both collect and deliver detections. Thus, in addition to high detection rates, it potentially offers very low equipment and installation cost.However, the long range and sparse nature of our opportunistically collected Wi-Fi transmissions presents a significant localization challenge. We propose a trajectory estimation method based on Viterbi's algorithm which takes second-by-second detections of a moving device as input, and produces the most likely spatio-temporal path taken. In addition, we present several methods that prompt passing devices to send additional messages, increasing detection rates an use signal-strength for improved accuracy.Based on our experimental evaluation from one 9-month deployment and several single-day deployments, passive WiFi tracking detects a large fraction of passing smartphones, and produces high-accuracy trajectory estimates.

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Section: Introductionmentioning

confidence: 99%

Tracking unmodified smartphones using wi-fi monitors

Musa

Eriksson

2012

Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems

219

176

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“…This can overcome the usual shortcoming of GPS, which struggles for precision and accuracy in indoor and densely built environments (Zarimpas et al 2006, Kawaguchi 2009, Xi et al 2010. Utilising this, we can easily and quickly estimate trajectories of the mobile devices (Musa and Eriksson 2012) which can be used similary to the GPS trajectories to understand individual travel patterns (Rekimoto et al 2007, Sapiezynski et al 2015, crowd behaviour (Abedi et al 2013, Mowafi et al 2013, vehicular (Lu et al 2010) and pedestrian movement (Xu et al 2013, Fukuzaki et al 2014. Such data can also be used in transportation planning and management to estimate travel time (Musa and Eriksson 2011) and real time traffic monitoring (Abbott-Jard et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Estimating real-time high-street footfall from Wi-Fi probe requests

Soundararaj

Cheshire

Longley

2019

International Journal of Geographical Information Science

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The accurate measurement of human activity with high spatial and temporal granularity is crucial for understanding the structure and function of the built environment. With increasing mobile ownership, the Wi-Fi 'probe requests' generated by mobile devices can act as a cheap, scalable and real-time source of data for establishing such measures. The two major challenges we face in using these probe requests for estimating human activity are: filtering the noise generated by the uncertain field of measurement, and clustering anonymised probe requests generated by the same devices together without compromising the privacy of the users. In this paper we demonstrate that we can overcome these challenges by using class intervals and a novel graph based technique for filtering and clustering the probe requests which in turn, enables us to reliably measure real-time pedestrian footfall at retail high streets.

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“…Literatürde bulunan akademik çalışmalar ve piyasada bulunan ticari ürünler incelendiğinde personel takibi [5], [6], [7], hasta takibi [8], [9], [10], [11] ve lojistik takibini [12], [13], [14] sağlayan sistemlerin yurt dışında mevcut olarak kullanıldığı görülmüştür.…”

Section: Introductionunclassified

“…Büyük ölçekli bir çözüm üzerinde bu sistem, yeraltı maden güvenliği ve haberleşme, yeraltı bina güvenliği ve haberleşme, kapalı / açık alan personel takibi, açık / kapalı alan hareketli varlık takibi, çevresel test, ölçüm ile sürekli veri kaydı ve güvenlik kaygıları için kullanılabilmektedir. Çalışma [7] …”

Section: Introductionunclassified

Comprehensive indoor remote tracking system

Yayan

Inan

Guner

et al. 2015

2015 23nd Signal Processing and Communications Applications Conference (SIU)

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Özetçe-Günümüzde çok katlı, karmaşık ve geniş alanlara yayılmış kapalı mekânların sayısı gittikçe artmaktadır. Bu nedenle iç ortamlarda konum tabanlı takip sistemleri önemli kazanmıştır. Ülkemizde iç ortamlarda takibi sistem ve servisler bulunmamaktadır. Bu çalışmada İç Ortamlarda Uzaktan Takip Amaçlı Konumlandırma Sistemi (U-TAKİP) geliştirilmiştir. Ortama kurulan algılayıcı ağı ile K-NN algoritması kullanılarak merkezi sunucu üzerinde uzaktan takip (kişi/nesne) sistemi geliştirilmiştir. Sinyal kaynağı olarak giyilebilir veya taşınabilir Bluetooth Low Energy etiketler kullanılmaktadır. Uygulamanın kullanım alanları; personel takibi, hasta takibi, lojistik takibi olarak belirlenmiştir. Anahtar Kelimeler-iç ortamlarda konumlandırma; bluetooth low energy; kablosuz algılayıcı ağları; personel takibi; hasta takibi; lojistik takibi; uzaktan takip; web programlama.Abstract-Nowadays, the number of indoor areas which are multi-layered, complex and spread to large areas is increasing. Because of that, location-based tracking systems have become an important place in the indoor areas. In our country, there isn't any system and service which provides tracking for indoor locations. In this study, indoor positioning system for remote tracking (U-TAKİP) has been developed. Using sensor network which will be set in the area and K-NN algorithm on the central server remote tracking (people / objects) system has been developed. As signal resource wearable or portable Bluetooth Low Energy tags was used. Use areas of the application is defined as personnel tracking, patient tracking, child tracking, logistics tracking.

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Vehicle Tracking Using Particle Filter in Wi-Fi Network

Cited by 9 publications

References 10 publications

Tracking unmodified smartphones using wi-fi monitors

Tracking unmodified smartphones using wi-fi monitors

Estimating real-time high-street footfall from Wi-Fi probe requests

Comprehensive indoor remote tracking system

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