On the Existence of an MVU Estimator for Target Localization with Censored, Noise Free Binary Detectors

The problem of target localization with censored noise free binary detectors is considered. In this setting only the detecting sensors report their locations to the fusion center. It is proven that if the radius of detection is not known to the fusion center, a minimum variance unbiased (MVU) estimator does not exist. Also it is shown that when the radius is known the center of mass of the possible target region is the MVU estimator. In addition, a sub-optimum estimator is introduced whose performance is close to the MVU estimator but is preferred computationally. Furthermore, minimal sufficient statistics have been provided, both when the detection radius is known and when it is not. Simulations confirmed that the derived MVU estimator outperforms several heuristic location estimators.Comment: 25 pages, 9 figure

LEDs assisted navigation in connected cars

Dissertação de natureza científica para obtenção do grau de Mestre em Engenharia Eletrónica e TelecomunicaçõesAlternative wireless technologies are needed due to the increasing traffic demand and the shortage of RF band. VLC uses the visible light spectrum to encode and transmit information and is a complement to RF, providing additional bandwidth. Traffic lights are the main infrastructures to control access to roads and will soon be replaced by more efficient structures to improve traffic management. The goal of this dissertation is the characterization and test of communication links based on VLC technology for road management applications. Transmitters of the VLC link are tetrachromatic white LEDs used for illumination and data transmission. The characterization of the optical transmitter system is done through MATLAB simulations, using the Lambertian model. Receivers based on a-SiC:H/a-Si:H photodiodes with selective spectral sensitivity are used to. The studied scenario is a crossroad formed by five cells, with a LED at each corner providing a certain coverage and forming nine footprints. The OOK modulation was used, and the transmitted message follows a 64-bit frame structure. The coverage map and footprint map were obtained as outputs. A calibration curve was used in the encoding and decoding process. Two trajectories were tested: vehicle moving from West to East and from West to North. The encoded process was successful, proving that the simulation tool developed produces valid results. The decoding process was successful with the simulated results but not so much with the signals measured in the laboratory. The red LED/channel presented the least error followed by the green, since these are more distinguishable. The blue and violet LED/channel are less distinguishable and presented the most errors. Adjusting the calibration curve or implementing error detection mechanism are proposed as solutions. A GUI was developed to enable easy interaction between the user and the simulation tool.Devido ao aumento da procura de tráfego e diminuição da banda RF disponível são necessárias tecnologias sem fios alternativas. O VLC utiliza o espetro visível para codificar e transmitir informação, sendo um complemento ao RF fornecendo largura de banda adicional. Os semáforos são as principais infraestruturas de controlo de acesso às estradas e serão eventualmente substituídas por estruturas mais eficiente para melhorar a gestão do trânsito. O objetivo desta dissertação é a caracterização e teste da comunicação utilizando a tecnologia VLC em aplicações de gestão rodoviária. Os transmissores usados para iluminação e comunicação são LEDs tetra-cromáticos. A caracterização do transmissor ótico foi realizada em MATLAB usando o modelo Lambertiano. O recetor utilizado é um foto-detetor baseado em estruturas pin de a-SiC:H e a-Si:H que apresentam sensibilidade espectral seletiva. O cenário estudado é um cruzamento formado por cinco células, com um LED em cada canto, proporcionando uma cobertura específica e formando em conjunto nove footprints. Foi usada a modulação OOK e a mensagem enviada utiliza uma estrutura de 64 bits. Como resultados, foram obtidos mapas de cobertura e de footprints. A curva de calibração foi usada para o processo de codificação e descodificação. Foram testadas duas trajetórias: veículos provenientes de Oeste para Este e de Oeste para Norte. O processo de codificação foi bem-sucedido, mostrando que a ferramenta de simulação desenvolvida produz resultados válidos. O processo de descodificação foi bem-sucedido para os resultados simulados, mas apresenta erros para as medidas laboratoriais. O LED/canal vermelho apresentou menos erros, seguido do verde pois estes são mais distinguíveis. O azul e o violeta são menos distinguíveis, apresentando mais erros. As soluções propostas são ajustar a curva de calibração ou implementar de mecanismos de deteção de erros. Foi desenvolvida uma interface gráfica para facilitar a interação entre utilizador e ferramenta de simulação.info:eu-repo/semantics/publishedVersio

A two phase framework for visible light-based positioning in an indoor environment: performance, latency, and illumination

Recently with the advancement of solid state lighting and the application thereof to Visible Light Communications (VLC), the concept of Visible Light Positioning (VLP) has been targeted as a very attractive indoor positioning system (IPS) due to its ubiquity, directionality, spatial reuse, and relatively high modulation bandwidth. IPSs, in general, have 4 major components (1) a modulation, (2) a multiple access scheme, (3) a channel measurement, and (4) a positioning algorithm. A number of VLP approaches have been proposed in the literature and primarily focus on a fixed combination of these elements and moreover evaluate the quality of the contribution often by accuracy or precision alone. In this dissertation, we provide a novel two-phase indoor positioning algorithmic framework that is able to increase robustness when subject to insufficient anchor luminaries and also incorporate any combination of the four major IPS components. The first phase provides robust and timely albeit less accurate positioning proximity estimates without requiring more than a single luminary anchor using time division access to On Off Keying (OOK) modulated signals while the second phase provides a more accurate, conventional, positioning estimate approach using a novel geometric constrained triangulation algorithm based on angle of arrival (AoA) measurements. However, this approach is still an application of a specific combination of IPS components. To achieve a broader impact, the framework is employed on a collection of IPS component combinations ranging from (1) pulsed modulations to multicarrier modulations, (2) time, frequency, and code division multiple access, (3) received signal strength (RSS), time of flight (ToF), and AoA, as well as (4) trilateration and triangulation positioning algorithms. Results illustrate full room positioning coverage ranging with median accuracies ranging from 3.09 cm to 12.07 cm at 50% duty cycle illumination levels. The framework further allows for duty cycle variation to include dimming modulations and results range from 3.62 cm to 13.15 cm at 20% duty cycle while 2.06 cm to 8.44 cm at a 78% duty cycle. Testbed results reinforce this frameworks applicability. Lastly, a novel latency constrained optimization algorithm can be overlaid on the two phase framework to decide when to simply use the coarse estimate or when to expend more computational resources on a potentially more accurate fine estimate. The creation of the two phase framework enables robust, illumination, latency sensitive positioning with the ability to be applied within a vast array of system deployment constraints

Indoor positioning with deep learning for mobile IoT systems

2022 Summer.Includes bibliographical references.The development of human-centric services with mobile devices in the era of the Internet of Things (IoT) has opened the possibility of merging indoor positioning technologies with various mobile applications to deliver stable and responsive indoor navigation and localization functionalities that can enhance user experience within increasingly complex indoor environments. But as GPS signals cannot easily penetrate modern building structures, it is challenging to build reliable indoor positioning systems (IPS). Currently, Wi-Fi sensing based indoor localization techniques are gaining in popularity as a means to build accurate IPS, benefiting from the prevalence of 802.11 family. Wi-Fi fingerprinting based indoor localization has shown remarkable performance over geometric mapping in complex indoor environments by taking advantage of pattern matching techniques. Today, the two main information extracted from Wi-Fi signals to form fingerprints are Received Signal Strength Index (RSSI) and Channel State Information (CSI) with Orthogonal Frequency-Division Multiplexing (OFDM) modulation, where the former can provide the average localization error around or under 10 meters but has low hardware and software requirements, while the latter has a higher chance to estimate locations with ultra-low distance errors but demands more resources from chipsets, firmware/software environments, etc. This thesis makes two novel contributions towards realizing viable IPS on mobile devices using RSSI and CSI information, and deep machine learning based fingerprinting. Due to the larger quantity of data and more sophisticated signal patterns to create fingerprints in complex indoor environments, conventional machine learning algorithms that need carefully engineered features suffer from the challenges of identifying features from very high dimensional data. Hence, the abilities of approximation functions generated from conventional machine learning models to estimate locations are limited. Deep machine learning based approaches can overcome these challenges to realize scalable feature pattern matching approaches such as fingerprinting. However, deep machine learning models generally require considerable memory footprint, and this creates a significant issue on resource-constrained devices such as mobile IoT devices, wearables, smartphones, etc. Developing efficient deep learning models is a critical factor to lower energy consumption for resource intensive mobile IoT devices and accelerate inference time. To address this issue, our first contribution proposes the CHISEL framework, which is a Wi-Fi RSSI- based IPS that incorporates data augmentation and compression-aware two-dimensional convolutional neural networks (2D CAECNNs) with different pruning and quantization options. The proposed model compression techniques help reduce model deployment overheads in the IPS. Unlike RSSI, CSI takes advantages of multipath signals to potentially help indoor localization algorithms achieve a higher level of localization accuracy. The compensations for magnitude attenuation and phase shifting during wireless propagation generate different patterns that can be utilized to define the uniqueness of different locations of signal reception. However, all prior work in this domain constrains the experimental space to relatively small-sized and rectangular rooms where the complexity of building interiors and dynamic noise from human activities, etc., are seldom considered. As part of our second contribution, we propose an end-to-end deep learning based framework called CSILoc for Wi-Fi CSI-based IPS on mobile IoT devices. The framework includes CSI data collection, clustering, denoising, calibration and classification, and is the first study to verify the feasibility to use CSI for floor level indoor localization with minimal knowledge of Wi-Fi access points (APs), thus avoiding security concerns during the offline data collection process

Design and Implementation of an RSSI-Based Bluetooth Low Energy Indoor Localization System

Indoor Positioning System (IPS) is a crucial technology that enables medical staff and hospital managements to accurately locate and track persons or assets inside the medical buildings. Among other technologies, Bluetooth Low Energy (BLE) can be exploited for achieving an energy-efficient and low-cost solution. This work presents the design and implementation of an received signal strength indicator (RSSI)-based indoor localization system. The paper shows the implementation of a low complex weighted k-Nearest Neighbors algorithm that processes raw RSSI data from connection-less iBeacon's. The designed hardware and firmware are implemented around the low-power and low-cost nRF52832 from Nordic Semiconductor. Experimental evaluation with the real-time data processing has been evaluated and presented in a 7.2 m by 7.2 m room with furniture and 5 beacon nodes. The experimental results show an average error of only 0.72 m in realistic conditions. Finally, the overall power consumption of the fixed beacon with a periodic advertisement of 100 ms is only 50 uA at 3 V, which leads to a long-lasting solution of over one year with a 500 mAh coin battery.Comment: This article has been accepted for publication in the proceedings of the 2021 IEEE International Conference on Wireless and Mobile Computing, Networking And Communications (WiMob). DOI: 10.1109/WiMob52687.2021.960627