Experimental Evaluation of a Machine Learning-Based RSS Localization Method Using Gaussian Processes and a Quadrant Photodiode

The research interest on indoor Location-Based Services (LBS) has increased during the last years, especially using LED lighting, since they can deal with the dual functionality of lighting and localization with centimetric accuracy. There are several positioning approaches using lateration and angular methods. These methods typically rely on the physical model to deal with the multipath effect, environmental fluctuations, calibration of the optical setup, etc. A recent approach is the use of Machine Learning (ML) techniques. ML techniques provide accurate location estimates based on observed data without requiring the underlying physical model to be described. This work proposes an optical indoor local positioning system based on multiple LEDs and a quadrant photodiode plus an aperture. Different frequencies are used to allow the simultaneous emission of all transmitted signals and their processing at the receiver. For that purpose, two algorithms are developed. First, a triangulation algorithm based on Angle of Arrival (AoA) measurements, which uses the Received Signal Strength (RSS) values from every LED on each quadrant to determine the image points projected from each emitter on the receiver and, then, implements a Least Squares Estimator (LSE) and trigonometric considerations to estimate the receiver?s position. Secondly, the performance of a data-driven approach using Gaussian Processes is evaluated. The proposals have been experimentally validated in an area of 3 × 3m2 and a height of 1.3m (distance from transmitters to receiver). The experimental tests achieve p50 and p95 2D absolute errors below 9.38 cm and 21.94 cm for the AoA-based triangulation algorithm, and 3.62 cm and 16.65 cm for the Gaussian Processes.Agencia Estatal de Investigació

Statistical Analysis of Geometric Algorithms in Vehicular Visible Light Positioning

Vehicular visible light positioning (VLP) methods find relative locations of vehicles by estimating the positions of intensity-modulated head/tail lights of one vehicle (target) with respect to another (ego). Estimation is done in two steps: 1) relative bearing or range of the transmitter-receiver link is measured over the received signal on the ego side, and 2) target position is estimated based on those measurements using a geometric algorithm that expresses position coordinates in terms of the bearing-range parameters. The primary source of statistical error for these non-linear algorithms is the channel noise on the received signals that contaminates parameter measurements with varying levels of sensitivity. In this paper, we present two such geometric vehicular VLP algorithms that were previously unexplored, compare their performance with state-of-the-art algorithms over simulations, and analyze theoretical performance of all algorithms against statistical channel noise by deriving the respective Cramer-Rao lower bounds. The two newly explored algorithms do not outperform existing state-of-the-art, but we present them alongside the statistical analyses for the sake of completeness and to motivate further research in vehicular VLP. Our main finding is that direct bearing-based algorithms provide higher accuracy against noise for estimating lateral position coordinates, and range-based algorithms provide higher accuracy in the longitudinal axis due to the non-linearity of the respective geometric algorithms.Comment: Technical report. 7 pages, 4 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

Towards joint communication and sensing (Chapter 4)

Localization of user equipment (UE) in mobile communication networks has been supported from the early stages of 3rd generation partnership project (3GPP). With 5th Generation (5G) and its target use cases, localization is increasingly gaining importance. Integrated sensing and localization in 6th Generation (6G) networks promise the introduction of more efficient networks and compelling applications to be developed

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

A Survey on Fundamental Limits of Integrated Sensing and Communication

The integrated sensing and communication (ISAC), in which the sensing and communication share the same frequency band and hardware, has emerged as a key technology in future wireless systems due to two main reasons. First, many important application scenarios in fifth generation (5G) and beyond, such as autonomous vehicles, Wi-Fi sensing and extended reality, requires both high-performance sensing and wireless communications. Second, with millimeter wave and massive multiple-input multiple-output (MIMO) technologies widely employed in 5G and beyond, the future communication signals tend to have high-resolution in both time and angular domain, opening up the possibility for ISAC. As such, ISAC has attracted tremendous research interest and attentions in both academia and industry. Early works on ISAC have been focused on the design, analysis and optimization of practical ISAC technologies for various ISAC systems. While this line of works are necessary, it is equally important to study the fundamental limits of ISAC in order to understand the gap between the current state-of-the-art technologies and the performance limits, and provide useful insights and guidance for the development of better ISAC technologies that can approach the performance limits. In this paper, we aim to provide a comprehensive survey for the current research progress on the fundamental limits of ISAC. Particularly, we first propose a systematic classification method for both traditional radio sensing (such as radar sensing and wireless localization) and ISAC so that they can be naturally incorporated into a unified framework. Then we summarize the major performance metrics and bounds used in sensing, communications and ISAC, respectively. After that, we present the current research progresses on fundamental limits of each class of the traditional sensing and ISAC systems. Finally, the open problems and future research directions are discussed