Indoor position estimation using image sensor based on VLC

Moon, Myoung-geun; Choi, Sangdun

doi:10.1109/atc.2014.7043346

Cited by 16 publications

(8 citation statements)

References 6 publications

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“…On the other hand, positioning in indoor environment is even more demanding on accuracy and GNSS receivers are commonly not able to estimate position in these environments. Currently there has been a lot of different systems proposed to solve indoor positioning problem, based on radio networks [3][4][5][6], inertial measurements [7,8], image processing [9], or magnetic measurements [10].…”

Section: Introductionmentioning

confidence: 99%

Scalability Optimization of Seamless Positioning Service

Machaj¹,

Brída²,

Benikovsky³

2016

Mobile Information Systems

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Recently positioning services are getting more attention not only within research community but also from service providers. From the service providers point of view positioning service that will be able to work seamlessly in all environments, for example, indoor, dense urban, and rural, has a huge potential to open new markets. However, such system does not only need to provide accurate position estimates but have to be scalable and resistant to fake positioning requests. In the previous works we have proposed a modular system, which is able to provide seamless positioning in various environments. The system automatically selects optimal positioning module based on available radio signals. The system currently consists of three positioning modules—GPS, GSM based positioning, and Wi-Fi based positioning. In this paper we will propose algorithm which will reduce time needed for position estimation and thus allow higher scalability of the modular system and thus allow providing positioning services to higher amount of users. Such improvement is extremely important, for real world application where large number of users will require position estimates, since positioning error is affected by response time of the positioning server.

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Section: Introductionmentioning

confidence: 99%

Scalability Optimization of Seamless Positioning Service

Machaj¹,

Brída²,

Benikovsky³

2016

Mobile Information Systems

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“…An increasing number of studies have explored a variety of schemes for indoor positioning using LEDs and image sensors [23][24][25][26][27][28][29][30][31]. Positioning systems use LEDs for data transmission, and image sensors for demodulating the received light.…”

Section: Introductionmentioning

confidence: 99%

Improved visible light communication positioning algorithm based on image sensor tilting at room corners

Zhu

et al. 2018

IET Communications

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“…The mean error distance was 4.4 cm. The AOA method of visible light positioning provides decimeter accuracy, but it depends on a receiver to receive signals from at least three transmitters to do so …”

Section: Introductionmentioning

confidence: 99%

A high accuracy and efficiency indoor positioning system with visible light communication

Chen

2018

Trans Emerging Tel Tech

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Positioning technology is important to the development of location‐based services. This work proposes a highly accurate and efficient indoor positioning system that uses visible light communication technology. The system uses a fingerprinting positioning mechanism that is based on a mobile device. A dynamic k‐NN (DKNN) mechanism is developed to improve upon k‐nearest neighbor (k‐NN) positioning, which is based on fingerprinting. This work also proposes an integrated angle of arrival (AOA) and fingerprinting mechanism for three‐dimensional positioning, which reduces the time that would otherwise be required for fingerprinting training and improves the accuracy of the indoor positioning system. When used for fixed‐height two‐dimensional positioning, the mean error distance of the DKNN fingerprinting positioning mechanism is approximately 0.15 cm. The AOA mean error distance is approximately 11.43 cm. The traditional k‐NN mean error distance is approximately 1.47 cm. Ignoring the height error, the integrated AOA and fingerprinting positioning mechanisms have a mean error distance of approximately 2.43 cm; the traditional AOA mean error distance is approximately 11.10 cm, and the fingerprinting mean error distance is approximately 2.74 cm. When the height error is considered, the AOA and fingerprinting fusion positioning mechanisms have a mean error distance of approximately 2.83 cm; the AOA mean error distance is approximately 11.62 cm, and the fingerprinting mean error distance is approximately 4.15 cm. The experimental results therefore show that the proposed mechanism provides the highest positioning accuracy.

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Indoor position estimation using image sensor based on VLC

Cited by 16 publications

References 6 publications

Scalability Optimization of Seamless Positioning Service

Scalability Optimization of Seamless Positioning Service

Improved visible light communication positioning algorithm based on image sensor tilting at room corners

A high accuracy and efficiency indoor positioning system with visible light communication

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