A Survey of Positioning Systems Using Visible LED Lights

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.As Global Positioning System (GPS) cannot provide satisfying performance in indoor environments, indoor positioning technology, which utilizes indoor wireless signals instead of GPS signals, has grown rapidly in recent years. Meanwhile, visible light communication (VLC) using light devices such as light emitting diodes (LEDs) has been deemed to be a promising candidate in the heterogeneous wireless networks that may collaborate with radio frequencies (RF) wireless networks. In particular, light-fidelity has a great potential for deployment in future indoor environments because of its high throughput and security advantages. This paper provides a comprehensive study of a novel positioning technology based on visible white LED lights, which has attracted much attention from both academia and industry. The essential characteristics and principles of this system are deeply discussed, and relevant positioning algorithms and designs are classified and elaborated. This paper undertakes a thorough investigation into current LED-based indoor positioning systems and compares their performance through many aspects, such as test environment, accuracy, and cost. It presents indoor hybrid positioning systems among VLC and other systems (e.g., inertial sensors and RF systems). We also review and classify outdoor VLC positioning applications for the first time. Finally, this paper surveys major advances as well as open issues, challenges, and future research directions in VLC positioning systems.Peer reviewe

Monte Carlo algorithm for the evaluation of the distance estimation variance in RSS-based visible light positioning

In this work, the Monte Carlo algorithm to determine the variance on the distance estimation in Received Signal Strength-based visible light positioning is considered. The method is build on the maximization of the signal-to-noise-ratio by means of matched filtering, and leads to a number of characteristics that are typically only obtained after intensive analytical elaborations. It is shown that the results match those obtained by calculating the Cramer-Rao lower bound when only the noise is considered as non-deterministic. It is demonstrated that the method is also applicable when multiple physical parameters exhibit a probability distribution, leading to an assessment of the distance estimation accuracy in more realistic settings

Optical Channel Impulse Response-Based Localization Using An Artificial Neural Network

Visible light positioning has the potential to yield sub-centimeter accuracy in indoor environments, yet conventional received signal strength (RSS)-based localization algorithms cannot achieve this because their performance degrades from optical multipath reflection. However, this part of the optical received signal is deterministic due to the often static and predictable nature of the optical wireless channel. In this paper, the performance of optical channel impulse response (OCIR)-based localization is studied using an artificial neural network (ANN) to map embedded features of the OCIR to the user equipment's location. Numerical results show that OCIR-based localization outperforms conventional RSS techniques by two orders of magnitude using only two photodetectors as anchor points. The ANN technique can take advantage of multipath features in a wide range of scenarios, from using only the DC value to relying on high-resolution time sampling that can result in sub-centimeter accuracy

Cellular Underwater Wireless Optical CDMA Network: Potentials and Challenges

Underwater wireless optical communications is an emerging solution to the expanding demand for broadband links in oceans and seas. In this paper, a cellular underwater wireless optical code division multiple-access (UW-OCDMA) network is proposed to provide broadband links for commercial and military applications. The optical orthogonal codes (OOC) are employed as signature codes of underwater mobile users. Fundamental key aspects of the network such as its backhaul architecture, its potential applications and its design challenges are presented. In particular, the proposed network is used as infrastructure of centralized, decentralized and relay-assisted underwater sensor networks for high-speed real-time monitoring. Furthermore, a promising underwater localization and positioning scheme based on this cellular network is presented. Finally, probable design challenges such as cell edge coverage, blockage avoidance, power control and increasing the network capacity are addressed.Comment: 11 pages, 10 figure

On the Performance Gain of Harnessing Non-Line-Of-Sight Propagation for Visible Light-Based Positioning

In practice, visible light signals undergo non-line-of-sight (NLOS) propagation, and in visible light-based positioning (VLP) methods, the NLOS links are usually treated as disturbance sources to simplify the associated signal processing. However, the impact of NLOS propagation on VLP performance is not fully understood. In this paper, we aim to reveal the performance limits of VLP systems in an NLOS propagation environment via Fisher information analysis. Firstly, the closed-form Cramer-Rao lower bound (CRLB) on the estimation error of user detector (UD) location and orientation is established to shed light on the NLOS-based VLP performance limits. Secondly, the information contribution from the NLOS channel is quantified to gain insights into the effect of the NLOS propagation on the VLP performance. It is shown that VLP can gain additional UD location information from the NLOS channel via leveraging the NLOS propagation knowledge. In other words, the NLOS channel can be exploited to improve VLP performance in addition to the line-of-sight (LOS) channel. The obtained closed-form VLP performance limits can not only provide theoretical foundations for the VLP algorithm design under NLOS propagation, but also provide a performance benchmark for various VLP algorithms

Sistemas de posicionamento baseados em comunicação por luz para ambientes interiores

The demand for highly precise indoor positioning systems (IPSs) is growing rapidly due to its potential in the increasingly popular techniques of the Internet of Things, smart mobile devices, and artificial intelligence. IPS becomes a promising research domain that is getting wide attention due to its benefits in several working scenarios, such as, industries, indoor public locations, and autonomous navigation. Moreover, IPS has a prominent contribution in day-to-day activities in organizations such as health care centers, airports, shopping malls, manufacturing, underground locations, etc., for safe operating environments. In indoor environments, both radio frequency (RF) and optical wireless communication (OWC) based technologies could be adopted for localization. Although the RF-based global positioning system, such as, Global positioning system offers higher penetration rates with reduced accuracy (i.e., in the range of a few meters), it does not work well in indoor environments (and not at all in certain cases such as tunnels, mines, etc.) due to the very weak signal and no direct access to the satellites. On the other hand, the light-based system known as a visible light positioning (VLP) system, as part of the OWC systems, uses the pre-existing light-emitting diodes (LEDs)-based lighting infrastructure, could be used at low cost and high accuracy compared with the RF-based systems. VLP is an emerging technology promising high accuracy, high security, low deployment cost, shorter time response, and low relative complexity when compared with RFbased positioning. However, in indoor VLP systems, there are some concerns such as, multipath reflection, transmitter tilting, transmitter’s position, and orientation uncertainty, human shadowing/blocking, and noise causing the increase in the positioning error, thereby reducing the positioning accuracy of the system. Therefore, it is imperative to capture the characteristics of different VLP channel and properly model them for the dual purpose of illumination and localization. In this thesis, firstly, the impact of transmitter tilting angles and multipath reflections are studied and for the first time, it is demonstrated that tilting the transmitter can be beneficial in VLP systems considering both line of sight (LOS) and non-line of sight transmission paths. With the transmitters oriented towards the center of the receiving plane, the received power level is maximized due to the LOS components. It is also shown that the proposed scheme offers a significant accuracy improvement of up to ~66% compared with a typical non-tilted transmitter VLP. The effect of tilting the transmitter on the lighting uniformity is also investigated and results proved that the uniformity achieved complies with the European Standard EN 12464-1. After that, the impact of transmitter position and orientation uncertainty on the accuracy of the VLP system based on the received signal strength (RSS) is investigated. Simulation results show that the transmitter uncertainties have a severe impact on the positioning error, which can be leveraged through the usage of more transmitters. Concerning a smaller transmitter’s position epochs, and the size of the training set. It is shown that, the ANN with Bayesian regularization outperforms the traditional RSS technique using the non-linear least square estimation for all values of signal to noise ratio. Furthermore, a novel indoor VLP system is proposed based on support vector machines and polynomial regression considering two different multipath environments of an empty room and a furnished room. The results show that, in an empty room, the positioning accuracy improvement for the positioning error of 2.5 cm are 36.1, 58.3, and 72.2 % for three different scenarios according to the regions’ distribution in the room. For the furnished room, a positioning relative accuracy improvement of 214, 170, and 100 % is observed for positioning error of 0.1, 0.2, and 0.3 m, respectively. Ultimately, an indoor VLP system based on convolutional neural networks (CNN) is proposed and demonstrated experimentally in which LEDs are used as transmitters and a rolling shutter camera is used as receiver. A detection algorithm named single shot detector (SSD) is used which relies on CNN (i.e., MobileNet or ResNet) for classification as well as position estimation of each LED in the image. The system is validated using a real-world size test setup containing eight LED luminaries. The obtained results show that the maximum average root mean square positioning error achieved is 4.67 and 5.27 cm with SSD MobileNet and SSD ResNet models, respectively. The validation results show that the system can process 67 images per second, allowing real-time positioning.A procura por sistemas de posicionamento interior (IPSs) de alta precisão tem crescido rapidamente devido ao seu interesse nas técnicas cada vez mais populares da Internet das Coisas, dispositivos móveis inteligentes e inteligência artificial. O IPS tornou-se um domínio de pesquisa promissor que tem atraído grande atenção devido aos seus benefícios em vários cenários de trabalho, como indústrias, locais públicos e navegação autónoma. Além disso, o IPS tem uma contribuição destacada no dia a dia de organizações, como, centros de saúde, aeroportos, supermercados, fábricas, locais subterrâneos, etc. As tecnologias baseadas em radiofrequência (RF) e comunicação óptica sem fio (OWC) podem ser adotadas para localização em ambientes interiores. Embora o sistema de posicionamento global (GPS) baseado em RF ofereça taxas de penetração mais altas com precisão reduzida (ou seja, na faixa de alguns metros), não funciona bem em ambientes interiores (e não funciona bem em certos casos como túneis, minas, etc.) devido ao sinal muito fraco e falta de acesso direto aos satélites. Por outro lado, o sistema baseado em luz conhecido como sistema de posicionamento de luz visível (VLP), como parte dos sistemas OWC, usa a infraestrutura de iluminação baseada em díodos emissores de luz (LEDs) pré-existentes, é um sistemas de baixo custo e alta precisão quando comprado com os sistemas baseados em RF. O VLP é uma tecnologia emergente que promete alta precisão, alta segurança, baixo custo de implantação, menor tempo de resposta e baixa complexidade relativa quando comparado ao posicionamento baseado em RF. No entanto, os sistemas VLP interiores, exibem algumas limitações, como, a reflexão multicaminho, inclinação do transmissor, posição do transmissor e incerteza de orientação, sombra/bloqueio humano e ruído, que têm como consequência o aumento do erro de posicionamento, e consequente redução da precisão do sistema. Portanto, é imperativo estudar as características dos diferentes canais VLP e modelá-los adequadamente para o duplo propósito de iluminação e localização. Esta tesa aborda, primeiramente, o impacto dos ângulos de inclinação do transmissor e reflexões multipercurso no desempenho do sistema de posicionamento. Demonstra-se que a inclinação do transmissor pode ser benéfica em sistemas VLP considerando tanto a linha de vista (LOS) como as reflexões. Com os transmissores orientados para o centro do plano recetor, o nível de potência recebido é maximizado devido aos componentes LOS. Também é mostrado que o esquema proposto oferece uma melhoria significativa de precisão de até ~66% em comparação com um sistema VLP de transmissor não inclinado típico. O efeito da inclinação do transmissor na uniformidade da iluminação também é investigado e os resultados comprovam que a uniformidade alcançada está de acordo com a Norma Europeia EN 12464-1. O impacto da posição do transmissor e incerteza de orientação na precisão do sistema VLP com base na intensidade do sinal recebido (RSS) foi também investigado. Os resultados da simulação mostram que as incertezas do transmissor têm um impacto severo no erro de posicionamento, que pode ser atenuado com o uso de mais transmissores. Para incertezas de posicionamento dos transmissores menores que 5 cm, os erros médios de posicionamento são 23.3, 15.1 e 13.2 cm para conjuntos de 4, 9 e 16 transmissores, respetivamente. Enquanto que, para a incerteza de orientação de um transmissor menor de 5°, os erros médios de posicionamento são 31.9, 20.6 e 17 cm para conjuntos de 4, 9 e 16 transmissores, respetivamente. O trabalho da tese abordou a investigação dos aspetos de projeto de um sistema VLP indoor no qual uma rede neuronal artificial (ANN) é utilizada para estimativa de posicionamento considerando um canal multipercurso. O estudo considerou a influência do ruído como indicador de desempenho para a comparação entre diferentes abordagens de projeto. Três algoritmos de treino de ANNs diferentes foram considerados, a saber, Levenberg-Marquardt, regularização Bayesiana e algoritmos de gradiente conjugado escalonado, para minimizar o erro de posicionamento no sistema VLP. O projeto da ANN foi otimizado com base no número de neurónios nas camadas ocultas, no número de épocas de treino e no tamanho do conjunto de treino. Mostrou-se que, a ANN com regularização Bayesiana superou a técnica RSS tradicional usando a estimação não linear dos mínimos quadrados para todos os valores da relação sinal-ruído. Foi proposto um novo sistema VLP indoor baseado em máquinas de vetores de suporte (SVM) e regressão polinomial considerando dois ambientes interiores diferentes: uma sala vazia e uma sala mobiliada. Os resultados mostraram que, numa sala vazia, a melhoria da precisão de posicionamento para o erro de posicionamento de 2.5 cm são 36.1, 58.3 e 72.2% para três cenários diferentes de acordo com a distribuição das regiões na sala. Para a sala mobiliada, uma melhoria de precisão relativa de posicionamento de 214, 170 e 100% é observada para erro de posicionamento de 0.1, 0.2 e 0.3 m, respetivamente. Finalmente, foi proposto um sistema VLP indoor baseado em redes neurais convolucionais (CNN). O sistema foi demonstrado experimentalmente usando luminárias LED como transmissores e uma camara com obturador rotativo como recetor. O algoritmo de detecção usou um detector de disparo único (SSD) baseado numa CNN pré configurada (ou seja, MobileNet ou ResNet) para classificação. O sistema foi validado usando uma configuração de teste de tamanho real contendo oito luminárias LED. Os resultados obtidos mostraram que o erro de posicionamento quadrático médio alcançado é de 4.67 e 5.27 cm com os modelos SSD MobileNet e SSD ResNet, respetivamente. Os resultados da validação mostram que o sistema pode processar 67 imagens por segundo, permitindo o posicionamento em tempo real.Programa Doutoral em Engenharia Eletrotécnic