871 research outputs found
Multiple Hypothesis Data Association for Multipath-Assisted Positioning
Global navigation satellite system denied scenarios such as urban canyons or indoors cause a need for alternative precise localization systems. Our approach uses terrestrial signals of opportunity in a multipath-assisted positioning scheme. In multipath-assisted positioning, each multipath component arriving at a receiver is treated as a line-of-sight signal from a virtual transmitter. While the locations of the virtual transmitters are unknown, they can be estimated simultaneously to the user position using a simultaneous localization and mapping (SLAM) approach. An essential feature of SLAM is data association. This paper addresses the data association problem in multipath-assisted positioning, i.e., the identification of correspondences among physical or virtual transmitters. If a user recognizes a previously observed transmitter, it can correct its own position estimate. We generalize a previous version of our multiple hypothesis tracking scheme for data association in multipath-assisted positioning and show by means of simulations how data association improves the positioning accuracy
Multipath assisted positioning using machine learning
The multipath propagation of the radio signal was considered a problem for
positioning systems that had to be eliminated. However, a groundbreaking new
approach called multipath assisted positioning caused a paradigm shift, where multipath propagation improves the positioning performance. Moreover, the multipath
assisted positioning algorithm called Channel-SLAM shows the possibility of using
a single physical transmitter in a multipath environment for positioning. In this
thesis, I open a discussion on some problems that have vital importance for multipath assisted positioning algorithms with a focus on pedestrian positioning. Using
the idea of multipath assisted positioning, I present a single frequency network
positioning algorithm. I evaluated the single frequency network-based positioning
algorithm for positioning in a real scenario using a terrestrial digital video broadcasting transmission. I propose a novel pedestrian transition model utilizing the
inertial measurements from a handheld inertial measurement unit. The proposed
pedestrian transition model improves the precision and reliability of the Channel-SLAM. Comparing the proposed transition model with the Rician transition model
previously used in Channel-SLAM quantifies the performance improvement. This
thesis proposes a joint data association technique that overcomes the strong dependence on the radio channel estimation algorithm used in Channel-SLAM. The
joint data association allows reusing the previously observed virtual transmitters
after an outage of multipath component tracking. The evaluation based on the
walking pedestrian scenario shows that the joint data association algorithm provides superior positioning precision. The virtual transmitter position estimation
yields a significant computational load in Channel-SLAM. I propose a method
that represents the virtual transmitter by a Gaussian mixture model and learns
its parameters. The evaluation shows that the proposed method outperforms the
previous approach while decreasing the computational load. Also, the current
methods for radio channel estimation yield a considerable computational load that
prohibits a real-time deployment. The thesis investigates the possibility of using
artificial neural networks trained to estimate the number of multipath components
and corresponding delays in a noisy measurement of a channel impulse response.
The artificial neural network-based delay estimator provides a superresolution performance and faster runtime than the classical approaches. The precision of the
trained artificial neural network architecture is evaluated and compared to the
Cramer-Rao lower bound theoretical limit and classical channel estimation algorithms
Multipath Assisted Positioning with Transmitter Visibility Information
In multipath assisted positioning, multipath components (MPCs) are regarded as line-of-sight (LoS) signals from virtual transmitters. Instead of trying to mitigate the influence of MPCs, the spatial information contained in MPCs is exploited for localization. The locations of the physical and virtual transmitters are in general unknown but can be estimated with simultaneous localization and mapping (SLAM). Recently, a multipath assisted positioning algorithm named Channel-SLAM for terrestrial radio signals has been introduced. It simultaneously tracks the position of a receiver and maps the locations of physical and virtual radio transmitters. Maps of estimated transmitter locations can be augmented by additional information. Within this paper, we propose to extend the Channel-SLAM algorithm by mapping information about the visibility of transmitters. A physical or virtual transmitter is visible, if its signal is received in a LoS condition. We derive a novel particle filter for Channel-SLAM that estimates and exploits visibility information on transmitters in addition to their locations. We show by means of simulations in an indoor scenario that our novel particle filter improves the positioning performance of Channel-SLAM considerably
Cooperative Estimation of Maps of Physical and Virtual Radio Transmitters
In multipath assisted positioning, the spatial information contained in multipath components (MPCs) is exploited, as MPCs are regarded as line-of-sight signals from virtual transmitters. The positions of physical and virtual transmitters can be estimated jointly with the receiver position with simultaneous localization and mapping (SLAM). In our multipath assisted positioning approach called Channel-SLAM, the estimates from a channel estimator are used in a Rao-Blackwellized particle filter which implements SLAM. While the original Channel-SLAM algorithm is a single-user positioning system, we present a comprehensive framework for cooperative Channel-SLAM within this paper. Users cooperate by exchanging maps of estimated transmitter locations. With prior information about the locations of physical and virtual transmitters, the positioning performance of the users increases significantly. The more users contribute to such a transmitter map, the more increases the positioning performance. With simulations in an indoor scenario, we show that the positioning performance is bounded for cooperative Channel-SLAM in the long run
User Tracking with Multipath Assisted Positioning-based Fingerprinting and Deep Learning
Multipath assisted positioning schemes allow localizing a user with only a single physical transmitter by treating multipath components (MPCs) as line-of-sight signals from virtual transmitters. The user position and the locations of the physical and virtual transmitters can be estimated jointly with simultaneous localization and mapping (SLAM). While such approaches often show very good positioning performance, they come at the cost of a high computational complexity. To reduce this complexity, multipath assisted positioning schemes based on SLAM may be combined with fingerprinting, where the fingerprints are features of the wireless radio channel. Within this paper, we present such an approach, where a deep neural network (DNN) is trained on data from a multipath assisted positioning scheme to predict the user position and the corresponding uncertainty from channel information. Based on the DNN, a Kalman filter can accurately and efficiently track the user position. We show by simulations that the positioning performance is improved by a factor of 1.5 while the computational complexity is crucially lower than that of multipath assisted positioning-based SLAM
Entropy of Transmitter Maps in Cooperative Multipath Assisted Positioning
In multipath assisted positioning, multipath components (MPCs) are regarded as line-of-sight (LoS) signals from virtual transmitters. The locations of physical and virtual transmitters are typically unknown, but can be estimated jointly with the location of a mobile terminal using simultaneous localization and mapping (SLAM). When users cooperate by exchanging maps of estimated positions of physical and virtual transmitters, the positioning performance can be improved drastically. Within this paper, we investigate such transmitter maps that are shared among users. We derive an approximation of the entropy of transmitter maps that is based on the unscented transform and analyze the evolution of this entropy over time. Our simulations indicate that the transmitter maps converge quickly
Indoor wireless communications and applications
Chapter 3 addresses challenges in radio link and system design in indoor scenarios. Given the fact that most human activities take place in indoor environments, the need for supporting ubiquitous indoor data connectivity and location/tracking service becomes even more important than in the previous decades. Specific technical challenges addressed in this section are(i), modelling complex indoor radio channels for effective antenna deployment, (ii), potential of millimeter-wave (mm-wave) radios for supporting higher data rates, and (iii), feasible indoor localisation and tracking techniques, which are summarised in three dedicated sections of this chapter
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
Visible light positioning system based on a quadrant photodiode and encoding techniques
Visible light positioning systems (VLPSs) are a feasible alternative to local positioning systems due to the technology improvement and massive use of light-emitting diodes (LEDs). Compared to other technologies, VLPSs can provide significant advantages, such as the achieved accuracy, although they still present some issues, mainly related to the reduced coverage area or the high computational load. This article proposes the design of a VLPS based on four LED lamps as transmitters and a quadrant photodiode angular diversity aperture (QADA) as a receiver. As the shape of the QADA is circular and the aperture to be installed over it is square, we derive the corresponding general equations to obtain the currents through the different pads of the QADA, regarding the angle of incidence of the light (and, inversely, how to estimate the angle of incidence from the measured currents). An encoding scheme based on 1023-bit Kasami sequences is proposed for every transmission from the LED lamps, thus providing multiple access capability and robustness against low signal-to-noise ratios and harsh conditions, such as multipath and near-far effect. A triangulation technique has been applied to estimate the receiver's position, by means of the least-squares estimator (LSE), together with some geometrical considerations. The proposal has been validated by simulation and by experimental tests, obtaining 3-D positioning average errors below 13 and 5.5 cm for separations between the transmitters' plane and the receiver of 2 and 1 m, respectively
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