Visible Light Positioning (VLP) uses modulated LED luminaries to help locate a receiver inside a building. Indoor positioning is becoming an increasingly important service, and visible light has many advantages over other technologies used in indoor positioning systems (IPS). However existing VLP approaches have major drawbacks in robustness that have hindered their ability to be commercially deployed. This work proposes and demonstrates a new way of using light signals in an indoor localization system, titled Angular Diversity Visible Light Positioning (AD-VLP). AD-VLP uses optics at the transmitter to create a structured overlapping light pattern that can be used for positioning. This method is shown to have several advantages over existing VLP approaches, including increased robustness over intensity based techniques while still using a single element receiver. This work also includes an experimental implementation of the proposed AD-VLP system using existing mobile device technology. The experiments prove that sub-meter accuracy is obtainable, even when the receiver is oriented away from the transmitter. / Thesis / Master of Applied Science (MASc)

Activities of daily living (ADLs) are everyday routine tasks which provide insight into the physical and cognitive wellbeing of older adults. ADLs are commonly self-reported to clinicians, which can lead to overestimation and underestimation of a patients’ functional abilities. Remote health monitoring is an emerging field aimed at utilizing technology for monitoring ADLs remotely, improving clinical accuracy and enabling older adults to age safely within their homes. In this dissertation, we report a Smart Home platform and two indoor positioning systems (IPSs) – (i) a hybrid Bluetooth Low Energy (BLE) and radar motion sensor system and (ii) a hybrid radio-frequency identification (RFID) and infrared (IR) range-finding system for tracking occupant mobility, the primary predictor of falls among older adults. For the Smart Home platform, the design methodology and technological features were explained. As for the IPSs’, position accuracy of multiple occupants within multiple rooms of a residential apartment was evaluated. The systems were also evaluated for cost, implementation ease, and scalability, which, upon reviewing literature, were identified as key metrics for developing an IPS for enabling aging in place. Both IPSs enforced a decentralized localization architecture and performed well, achieving high localization accuracy for multiple occupants. / Thesis / Master of Applied Science (MASc) / By 2031, the number of people aged 65 and over is expected to nearly double. This population shift is concerning for healthcare providers as limited resources become increasingly constrained. Resultantly, older adults, the largest consumers of healthcare, face longer wait times and reduced quality of care. Remote health monitoring is an emerging field aimed at utilizing technology for monitoring older adults within their homes. In this thesis, we report a Smart Home platform and two indoor positioning systems (IPSs) for tracking resident mobility, the primary predictor of falls among older adults. For the Smart Home platform, the design methodology and technological features were explained. As for the IPSs’, position accuracy of multiple occupants within multiple rooms of a residential apartment was evaluated. Upon reviewing literature system cost, implementation ease, and scalability, were identified as key metrics for developing an IPS for enabling aging in place. Both IPSs performed well, achieving high localization accuracy for multiple occupants.

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Infrastructure-based indoor geolocation systems utilizing a regular grid arrangement of sensors are being investigated for many applications in indoor wireless networks. One of the factors affecting the Quality of Estimation (i.e. location estimation accuracy) of these systems is node density. In this dissertation we study the effects of node density on indoor geolocation systems based on time of arrival (TOA). The effects of node density on the performance of various indoor communication networks (e.g. wireless LANs) in the presence of realistic indoor radio propagation models has been analyzed and reported in the literature. However, we have noted the lack of an equivalent analysis on the effects of node density on the performance of infrastructure-based indoor geolocation systems. The goal of this dissertation is to address this knowledge gap. Due to the complicated behavior of the indoor radio channel, the relationship between the node density and Quality of Estimation (QoE) is not straightforward. Specifically, QoE depends on factors such as the bandwidth used to make the TOA-based distance measurements, the existence of undetected direct path (UDP) conditions, and coverage. In this dissertation, we characterize these dependencies. We begin by characterizing the Quality of Estimation for closest-neighbor (CN), least-squares (LS) and weighted LS techniques in the presence of different node densities and a distance measurement error (DME) model based on ray tracing (RT) that was recently proposed in the literature. Then, we propose a new indoor geolocation algorithm, Closest Neighbor with TOA Grid (CN-TOAG), characterize its performance and show that it outperforms the existing techniques. We also propose an extension to this algorithm, known as Coverage Map Search (CMS) that allows it to be used in suboptimal coverage conditions (which we refer to as partial coverage conditions) that may prevent other TOA-based geolocation techniques from being used. We treat the partial coverage case by defining coverage probabilities and relating them to the average radius of coverage and dimensions of the indoor area. Next, we characterize the effects of node density on the performance of the CN-TOAG algorithm using a DME model based on UWB measurements, and show that node density and partial coverage are intimately linked together. Since this second DME model also allows for the effects of UDP conditions (which affect the quality of the link or QoL), we also characterize the effects of varying UDP conditions on the performance. Finally, we conclude the dissertation by presenting an analysis of fundamental performance bounds for infrastructure-based indoor geolocation, specifically focusing on the Cramer-Rao Lower Bound (CRLB).

Three main elements for an indoor positioning and navigation system design are the signal structure, the signal processing algorithm and the digital and RF prototype hardware. This thesis focuses on the design and development of RF prototype hardware. The signal structure being used in the precise positioning system discussed in this thesis is a Multicarrier-Ultra Wideband (MC-UWB) type signal structure. Unavailability of RF modules suitable for MC-UWB based systems, led to design and development of custom RF transmitter and receiver modules which can be used for extensive field testing. The lack of RF design guidelines for multicarrier positioning systems that operate over fractional bandwidth ranging from 10% to 25% makes the RF design challenging as the RF components are stressed using multicarrier signal in a way not anticipated by the designers. This thesis, first presents simulation based performance evaluation of impulse radio based and multicarrier based indoor positioning systems. This led to an important revelation that multicarrier based positioning system is preferred over impulse radio based positioning systems. Following this, ADS simulations for a direct upconversion transmitter and a direct downconversion receiver, using multicarrier signal structure is presented. The thesis will then discuss the design and performance of the 24% fractional bandwidth RF prototype transmitter and receiver custom modules. This optimized 24% fractional bandwidth RF design, under controlled testing environment demonstrates positioning accuracy improvement by 2-4 times over the initial 11% fractional bandwidth non-optimized RF design. The thesis will then present the results of various indoor wireless tests using the optimized RF prototype modules which led to better understanding of the issues in a field deployable indoor positioning system.

Localizar objetos ou pessoas no interior de um edifício é de grande interesse. Contudo, diferentemente do que ocorre no exterior de edificações, não há metodologia consagrada para a determinação da posição desses entes nos edifícios. Para o posicionamento em locais abertos existem tecnologias consolidadas, como GNSS (Global Navigation Satellite System), a dificuldade em fazê-lo em interiores é maior. Nesses casos, o GNSS não pode ser utilizado, pois os sinais de rádio dos satélites não conseguem penetrar através das estruturas, enquanto que outras tecnologias são apenas incipientes nesse quesito. Abordagens habituais para a resolução dessa demanda têm se baseado na utilização de propagadores das ondas de rádio do GNSS, no uso da potência de sinais de redes sem fio ou, ainda, no emprego de transmissores infravermelhos. No entanto, uma técnica diferente pode ser empreendida para essa finalidade. Usando-se a assinatura das potências de rádio das redes sem fio nas imediações e no interior da edificação, é possível criar um mapa com base nesses sinais, permitindo a determinação da posição de um objeto. No presente trabalho foram desenvolvidos um sistema para geração do mapa de sinais, com critério de parada e um método de cálculo de posicionamento. Procedeu-se, também, à análise de quatro critérios para o cálculo final da posição do objeto, baseados no uso da distância euclidiana com os conjuntos de roteadores disponíveis. Concluiu-se que, quando o mapa de sinais é pequeno, o posicionamento fracassou. Entretanto, quando a quantidade de sinais geradores do mapa aumenta, os resultados apresentaram melhora significativa, com resultados próximos a 100% de assertividade. Desse modo foi possível determinar uma estimativa boa para o número mínimo de roteadores presentes na base e estabelecer um critério de parada para a fase de criação do mapa de sinais. / Locate objects or person inside a building is a subject that matters. Although what occurs outdoor of a building, there is no consolidate methodology to define a position in an indoor environment. For outdoor positioning there is the GNSS (Global Navigation Satellite System) a consolidate technology for outdoor usage with a lot of challenges to make this work in an indoor environment. In such cases, the GNSS cannot be used due to the lack of the sattelite radio signal, which cannot penetrate the walls and buildings structures, meanwhile other technologies are just incipient. Usual approaches using this approach are based in propagation of the GNSS radio signal, wi-fi based location or infrared transmitter. A different technique can be applied for indoor positioning. Using the wireless fingerprint in an indoor environment we created a radio map based on those signals, allowing the location of a mobile station. In the present research, we developed a system that generates a radio map (with a stop criteria) and a positioning calculation method. We concluded that when the radio map is small (with only a few measurements) the method cannot calculate accurately. However, with more measurements, the method succeeded and have a performance near a 100% of precision. In this way it was posible to estimate a number of access points needed for a building\'s range and define the stop criteria used in the radio map phase.

This dissertation describes research concerned with the use of advanced measurement techniques for the control of robotic manufacturing processes. The work focused on improving the state of technology in the precision robotic machining of components within the aerospace manufacturing industry within Australia. Specific contributions are the development of schemes for the use of advanced measurement equipment in precision machining operations and to apply flexible manufacturing techniques in automated manufacturing. The outcome of the research enables placement of a robotic end effector to drill a hole with a positional accuracy of 300 micron, employing an Indoor Global Positioning System for control of the drilling process. This can be accomplished within a working area of 35 square metres where the robot system and/or part positions may be varied dynamically during the process. Large aerospace structures are capable of flexing during manufacturing operations due to their physical size and low modulus of rigidity. This research work provided a framework for determining the appropriate type of automation and metrology systems needed for dynamic control suited to the precision drilling of holes in large aerospace components.

As the demand of indoor Location-Based Services (LBSs) increases, there is a growing interest in developing an accurate indoor positioning and tracking system on mobile devices. The core location determination problem can be reformulated as a sparse natured problem and thus can be solved by applying the Compressive Sensing (CS) theory. This thesis proposes a compact received signal strength (RSS) based real-time indoor positioning and tracking systems using CS theory that can be implemented on personal digital assistants (PDAs) and smartphones, which are both limited in processing power and memory compared to laptops. The proposed tracking system, together with a simple navigation module is implemented on Windows Mobile-operated smart devices and their performance in different experimental sites are evaluated. Experimental results show that the proposed system is a lightweight real-time algorithm that performs better than other traditional fingerprinting methods in terms of accuracy under constraints of limited processing and memory resources.

As the demand of indoor Location-Based Services (LBSs) increases, there is a growing interest in developing an accurate indoor positioning and tracking system on mobile devices. The core location determination problem can be reformulated as a sparse natured problem and thus can be solved by applying the Compressive Sensing (CS) theory. This thesis proposes a compact received signal strength (RSS) based real-time indoor positioning and tracking systems using CS theory that can be implemented on personal digital assistants (PDAs) and smartphones, which are both limited in processing power and memory compared to laptops. The proposed tracking system, together with a simple navigation module is implemented on Windows Mobile-operated smart devices and their performance in different experimental sites are evaluated. Experimental results show that the proposed system is a lightweight real-time algorithm that performs better than other traditional fingerprinting methods in terms of accuracy under constraints of limited processing and memory resources.

The popularity of wireless sensor networks is constantly increasing, both for use instatic machine to machine environments as well as dynamic environments wherethe sensor nodes are carried by humans. Higher demands are put on real-timetracking algorithms of the sensor nodes, both in terms of accuracy and speed. This thesis addresses the issue of tracking persons wearing small sensor nodeswithin a radio network. Focus lies on fusing sensor data in an efficient way withconsideration to the computationally constrained sensor nodes. Different sensorsare stochastically modelled, evaluated, and fused to form an estimate of the person’sposition. The central approach to solve the problem is to use a dead reckoning methodby detecting steps taken by the wearer combined with an Inertial MeasurementUnit to calculate the heading of the person wearing the sensor node. To decreasethe unavoidable drift which is associated with a dead reckoning algorithm, a mapis successfully fused with the dead reckoning algorithm. The information from themap can to a large extent remove drift. The developed system can successfully track a person wearing a sensor nodein an office environment across multiple floors. This is done with only minorknowledge about the initial conditions for the user. The system can recover fromdivergence situations which increases the long term reliability. / Intresset för trådlösa sensornätverk ökar konstant, såväl för statiska maskintill-maskintillämpningar som för dynamiska miljöer där sensornoderna är burnaav människor. Allt högre krav ställs på positioneringsalgoritmer för sensornätverken,där både hög precision och låg beräkningstid ofta är krav. Denna rapport behandlar problemet med att bestämma positionen av personburnasensornoder. Rapportens fokus är att effektivt kombinera sensordatamed hänsyn till sensornodernas begränsade beräkningskapacitet. Olika sensorermodelleras stokastiskt, utvärderas och kombineras för att forma en skattning avsensornodens position. Den huvudsakliga metoden för att lösa problemet är att dödräkna sensornodbärarenssteg kombinerat med kompass och tröghetssensorer för att skattastegets riktning. En karta över byggnaden används för att reducera den annarsoundvikliga drift som härrör från dödräkning. Informationen från kartan visarsig i stor utsträckning kunna reducera den här driften. Det utvecklade systemet kan följa en person genom en kontorsmiljö somsträcker sig över flera våningsplan. Detta med enbart lite information om personensinitiala position. Systemet kan även återhämta sig från situationer däralgoritmen divergerar vilket ökar systemets pålitlighet på lång sikt.