Indoor visible light positioning system with multipath reflection analysis

Gu, Wenbin; Aminikashani, Mohammadreza; Kavehrad, M.

doi:10.1109/icce.2016.7430533

Cited by 18 publications

(10 citation statements)

References 10 publications

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“…For the noiseless overall link, in Ref. 26, localization error was 8 cm at a Ricean K factor of 6.3 dB, whereas in the proposed system, the average localization error can be 0.5 cm at a Ricean factor of 25 dB. For the noisy overall link, the results in the previous work showed that the average localization error decreases as the Ricean K factor increases.…”

Section: Comparison and Analysismentioning

confidence: 71%

“…Gu et al 26 6 × 6 × 3. a large FOV receiver (FOV ¼ 65 deg) in a low reflectivity (ρ ¼ 0.01) room can achieve a high localization accuracy of 8 mm. Finally, a comparison of the localization accuracy is made with the previous works, and the proposed localization scheme provides the better performance, owing to the high value of SNR and Ricean K factor.…”

Section: Discussionmentioning

confidence: 99%

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Accuracy analysis of indoor visible light communication localization system based on received signal strength in non-line-of-sight environments by using least squares method

Shi

Cheng

et al. 2019

Opt. Eng.

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For an indoor localization system based on visible light communication (VLC) by using received signal strength technique, visible light signals used for estimating the distances between each localization target and reference nodes suffer from non-line-of-sight (NLoS) signal propagation, which could introduce large errors in estimating their locations. Both line-of-sight (LoS) link and NLoS link are taken into account in a noisy VLC channel, and thus the NLoS signal and ambient noise are the sources of localization error. Ricean K factor is introduced to evaluate the relation between the value of NLoS signal and the quality of localization in the proposed system. Analytical expressions for the distance measurement error and the upper bound of localization error are derived by using the least squares method. The simulation results show that the estimation of localization error matches the distribution of Ricean K factor in the noisy overall link. The environmental parameters that can be used to decrease the value of Ricean K factor are also discussed in the simulations, which provide the reference of parameters for building an experimental demonstration of a VLC indoor localization system. A comparison is conducted with the previous works to demonstrate the good performance of our scheme.

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Section: Comparison and Analysismentioning

confidence: 71%

Section: Discussionmentioning

confidence: 99%

Accuracy analysis of indoor visible light communication localization system based on received signal strength in non-line-of-sight environments by using least squares method

Shi

Cheng

et al. 2019

Opt. Eng.

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“…Therefore, LPS relying on PD are affected by location errors (from a few cm to 1 m) in real deployments [150,[152][153][154]. Image sensors can instead solve optical interference by exploiting multiple pixels [155].…”

Section: Resultsmentioning

confidence: 99%

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Galisteo

Wang

et al. 2017

Proceedings of the 4th ACM Workshop on Visible Light Communication Systems

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viii large have to face. Still, when I joined, it felt like a small group of people that were excited to research and push the borders of knowledge, always laughing, joking, going to Piratas Rock Wey for lunch (the best restaurant in Madrid) and attending table tennis tournaments. I even won one of those! (I did technically not, but I got a T-shirt, which is nice). The coffee breaks, the memes, the discussions... Will be with me forever. Special thanks to all the people from IMDEA and Comunidad de Madrid that helped me with my communication activities, a not so valued part of the research life that I believe is one of the paths to bring science to society.The second group is the people from La Caixa scholarship. Getting the La Caixa grant was not only good for the monetary appraisal itself (although that was not bad) but also because of the people I met. The annual retreats that we did were a breath of fresh air where I could interact with non-engineer scientists, which is surprisingly tricky when you are an engineer. My admiration and love for them are immeasurable. And of course, to the people from La Caixa organization itself, they treated us way better than what I would have expected in the best of my dreams.As an honorable mention, I should thank all the people that I interacted with in University of Uppsala, Sweden. Coming from a mediterranean country, their warm hearts compensated the cold climatology.Of course, thank you to my girlfriend, my family, and friends to support me when I was not in my best moments and for celebrating with me the sweetest of my victories. This thesis is yours too.Finally, to anyone reading this starting its Ph.D.: the thesis is longer than it looks.Once you start, there are no breaks until you defend it or drop it, so try to find your pace, do not rush for an ending that is too far. Deadlines come and go, but your mental health is yours only. Take care of it; take care of yourself.

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“…The degree of mobility of the user also affects the quality of the location information produced [ 42 ] by VLC positioning. VLC-based positioning also suffers from multipath issues [ 94 ].…”

Section: Factors Affecting the Quality Of Location Informationmentioning

confidence: 99%

Location Information Quality: A Review

Ranasinghe

Kray

2018

Sensors

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The quality of location information is an important factor for location-based services (LBSs). In the literature, the quality of location information has been defined in different ways based on varying sets of aspects. The objectives of this paper are to review existing literature discussing location information quality and to provide a consistent framework for describing and dealing with location information quality. In particular, we review existing literature on different aspects of location information quality and on factors that affect location sensing technologies (and thus location information quality). Based on this review, we also propose a simple model for describing location information quality and a classification of the strategies for dealing with variations in the quality of location information. Designers of location sensing systems can use this model as a standard vocabulary for describing the quality of location information. The classification of strategies can be used by developers of LBSs apps to design alternative strategies for dealing with location information quality on three levels: sensor-level, algorithm-level, and application-level, which are aligned with the Location Stack model.

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Indoor visible light positioning system with multipath reflection analysis

Cited by 18 publications

References 10 publications

Accuracy analysis of indoor visible light communication localization system based on received signal strength in non-line-of-sight environments by using least squares method

Accuracy analysis of indoor visible light communication localization system based on received signal strength in non-line-of-sight environments by using least squares method

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Location Information Quality: A Review

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