The principal objectives of this research are: to investigate the performance of different fingerprint-based WiFi Indoor Positioning Systems (IPS), analyse historical long-term data signals, detection of signal change points and outliers; then present an enhanced method that generates temporal based fingerprints. The proposed method consists of analysing signal strength profiles over time and detecting points at which the profile behaviour changes. This methodology can be used to dynamically adjust the fingerprint based on environmental factors, and with this select the relevant Wireless Access Points (WAPs) to be used for fingerprinting. The use of an Exponentially Weighted Moving Average (EWMA) Control Chart is investigated for this purpose. A long-term analysis of the WiFi scenery is presented and used as a test-bed for evaluation of state-of-the-art fingerprinting techniques. Data was collected and analysed over a period of 18 months, with over 840 different WAPs detected in over 77,000 observations covering 47 different locations of varying characteristics. A fully functional IPS has been developed and the design and implementation is described in this thesis. The system allows the scanning and recording of WiFi signals in order to define the generation of temporal fingerprints that can create radio-maps, which then allow indoor positioning to occur. This thesis presents the theory behind the concept and develops the technology to create a testable implementation. Experiments and their evaluation are also included. Based on the timestamp experiments the proposed system shows there is still room level accuracy, with a reduction in radio-map size.

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.

To know your whereabouts is important and sometimes exact position is the determinant of success or not. Since the launch of the USA made Global Positioning System (GPS) in the late 1970s, global tracking has been available both for military and civilian use. Today, satellite based systems like the American GPS, European Galileo and the Russian GLONASS are the standard for basically any navigation or location application. However these systems are limited by the fact that they must have connection with multiple satellites in order to work. This means that satellite dependent systems are very limited in indoor environments. Despite this, there is no standard for indoor positioning systems (IPSs) even close to the satellite dependent systems when it regards distribution and accessibility for use where satellite dependent systems are limited or not working. However, this new age of connectivity provides a number of options for positioning indoors. There are some systems available, but very few of them provide good enough accuracy. This Master thesis evaluates different options for indoor positioning using technologies that are available in smart devices and smart phones, such as Wi-Fi and Bluetooth, and focuses on indoor positioning systems that can provide high accuracy. The current state of the technologies and the possible future for them, considering rising need and interest in indoor positioning systems are covered in this thesis. A selection of technologies and approaches are explored, tested in conditions designed for this purpose and evaluated to highlight their differences in approach, accuracy and usability. In order to achieve the goals of this thesis, a hybrid method of experimental research design and system development is selected as the main research method. The hybrid method is focused on experimental research, and is used to investigate if the accuracy of the positioning data can be affected and improved by tuning independent variables in an IPS. To be able to do this, an application for smart devices, such as android smartphones, is developed. The application developed is the heart of the conceptual crossover IPS (CCIPS), which is named Locantis, that is being developed and used in the experimental stages of this investigation. It is also used to test the hypothesis that a CCIPS could be a valid contender for replacing the established IPSs. The observations show how the accuracy and precision of the location data is affected by change to independent variables in an IPS and how well a CCIPS can meet real time requirement’s. The main conclusion is that changes to independent variables have greater impact on the precision than the accuracy for location data in an IPS and that a CCIPS in many cases are the sensible choice of IPS. / Att veta var du befinner dig är viktigt och ibland är exakt position avgörande för framgång. Sedan lanseringen av amerikanska Global Positioning System (GPS) i slutet av 1970 har global navigation varit tillgängliga för både militärt och civilt bruk. Idag är satellitbaserade system, som det amerikanska GPS, europeiska Galileo och det ryska GLONASS, standard för i princip alla navigering eller platsinformation. Men dessa system är begränsade av det faktum att de måste ha kontakt med flera satelliter för att fungera. Detta innebär att satellitberoende system är mycket begränsad i inomhusmiljöer. Trots detta finns det ingen standard för inomhus positioneringssystem (IPSS) som kan mäta sig med de satellitberoende systemen när det gäller distribution och tillgänglighet. Men denna nya generationens kommunikation ger ett antal alternativ för positionering inomhus. Det finns IPS tillgängliga idag, men väldigt få av dem kan tillhandahålla en hög noggrannhet på positioneringen. Detta examensarbete utvärderar olika alternativ för inomhuspositionering med teknologier som är tillgängliga i smarta enheter och smarta telefoner, som Wi-Fi och Bluetooth, fokus ligger på inomhus positioneringssystem som kan ge hög noggrannhet. Det aktuella läget för dessa teknologier och dess möjliga framtid, med tanke på stigande behov och intresse av inomhus positioneringssystem, behandlas i detta examensarbete. Ett urval av teknologier och metoder utforskas, testas under förhållanden utformade för detta ändamål, och utvärderas för att lyfta fram sina skillnader i tillvägagångssätt, noggrannhet och användbarhet. För att uppnå målen i detta examensarbete har en hybrid metod för experimentell designforskning och systemutveckling valts som huvudsaklig forskningsmetod. Hybridmetoden fokuserar på experimentell forskning och används för att undersöka om positioneringsdatas noggrannhet kan påverkas och förbättras genom att förändra oberoende variabler i ett IPS. För att kunna göra detta har en applikation för smarta enheter, så som Android, utvecklats. Applikationen som utvecklats är hjärtat i det konceptuella kombinations inomhuspositioneringssystem Locantis som utvecklas och används i det experimentella stadiet i denna undersökning. Den används också för att testa hypotesen att en CCIPS kunde vara en giltig kandidat för att ersätta den etablerade IPSer. Observationerna visa hur noggrannhet och precision av positioneringsdata påverkas av förändring på oberoende variabler i ett IPS och hur väl ett CCIPS kan uppfylla realtidskravet talet. Huvudslutsatsen är att förändringar på oberoende variabler har större inverkan på precision än noggrannheten för lokaliseringsuppgifter i en IPS och att ett CCIPS i många fall är det klokt val av IPS.

The main purpose of this thesis work is to find and compare different network characteristics of MPS (Mobile Positioning System) in the different wireless technology domains. Since decades ago MNO’s (Mobile Network Operators) added many new services based on the geographical areas of subscribers and their needs. Here we define wireless networks and go through different types of technologies and do the comparison when they collect different types of data for their location-based services and see if we could have the same accuracy with 2G (second generation) of mobile network as like as 3G (third generation) and higher. Finally, we will come up with a proposal for new age technology.

Indoor Positioning Systems lokaliserar människor och objekt inomhus med hjälp av minst tre kända referenspunkter. System för inomhuspositionering som använder kända referenspunkter kallas anchor-based lokalisering medan de som beräknar deras positioner själva kallas anchor-free lokalisering. Syftet med detta arbete är att utveckla en algoritm som är anpassad efter ett anchor-free lokaliseringssystem. Den ska vara oberoende av nätverksuppkopplingen, hårdvaran och hur avstånden mellan mottagare och sensorer beräknats. Utgångspunkten för algoritmen är enbart avstånd mellan en mottagare och tre sensorer vilket kan beskrivas som arbetets huvudsakliga problem. Algoritmen implementerades i Java med en simulering som återspeglar positioneringen i en perfekt miljö och sedan testas på en Android-applikation. Simuleringen tillåter användaren att rita ut flera mätpunkter som skapar en rutt. Dessa mätpunkter utnyttjas för att dynamiskt lokalisera referenspunkterna och mätpunkterna genom att hitta ett minsta avstånd mellan sensorerna. Dessa avstånd kan beskrivas som sidorna för en referenstriangel som möjliggör att ett koordinatsystem kan spännas upp. Resultatet av den empiriska studien visade en felmarginal mellan 0,3-6 m utan signalstörningar, vilket inte var tillräckligt noggrant. Efter att algoritmen implementerats lades fokus på en korrigering som kan itereras igenom för att uppskatta bättre mätvärden för referenstriangeln. Korrigeringen gav positiva resultat med lägre felmarginal. Arbetet kan vidareutvecklas genom att implementeras i ett verkligt IPS-system och algoritmen kan förbättras genom att skapa utökade funktioner som kan hantera fler än tre beacons.

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The work deals with the issue of indoor positiong system in the enviroment of the Android operating system, based on the efficient standard Bluetooth Low Energy, with conjunction of bluetooth beacons. The outcome of master‘s thesis is system topology, description of its components, standards needed for his creation and finally construction of the system.

The indoor positioning system is a series of networking systems used to monitor/locate objects at indoor area as opposed that of GPS which does the same at outdoor. The increase in the popularity of the Internet of Things made the demand for Bluetooth Low Energy technology more and more essential due to their compatibility in the smartphones which makes it to access easier. The BLE’s reliable signal and accuracy in calculating the distance has a cutting edge on others in IPS. In this thesis, the Bluetooth Low Energy indoor positioning system was designed and implemented in the office area, and the positionofIoTdevicesweremonitored. OntheIoTdevices,thebeaconswereplaced. And thesebeaconswerecoveringtheofficearea. Thereceiver,smartphoneinourcase,recorded theReceivedSignalStrengthIndicationofthetransmittedsignalsfromthebeaconswithin the range of the signal and stored the collected data in a database. Two experiments have beenconducted. Oneisforbeaconsthatarestationaryandonethatismoving. Toevaluate these experiments, a few tests were performed to predict the position of beacons based on therecordedreceivedsignalstrength’s. Inthecaseofstationarybeacons, itoffersaccuracy range from 1 m to 5 m, and 3 m to 9.5 m in anticipating the position of each beacon in the case of moving beacon. This methodology was a mixture of fingerprinting and an algorithm of multilateration. Finally, the experiments show that the algorithm used provides the most accurate indoor position using BLE beacons that can be monitored through an Android-based application in real-time.

Context: Indoor positioning systems have become more widespread over the past decade, mainly due to devices such as Bluetooth Low Energy beacons which are low at cost and work effectively. The context of this thesis is to localize and help people navigate to the office equipment, meeting rooms, etc., in an office environment using machine learning algorithms. This can help the employees to work more effectively and conveniently saving time. Objective: To perform a literature review of various machine learning models in indoor positioning that are suitable for an office environment. Also, to experiment with those selected models and compare the results based on their performance. Android smartphone and BLE beacons have been used to collect RSSI values along with their respective location coordinates for the dataset. Besides, the accuracy of positioning is determined by using state-of-the-art machine learning algorithms to train the dataset. Using performance metrics such as Euclidean distance error, CDF curve of Euclidean distance error, RMSE and MAE to compare results and select the best model for this research. Methods: A Fingerprinting method for indoor positioning is studied and applied for the collection of the RSSI values and (x, y) location coordinates from the fixed beacons. A literature review is performed on various machine learning models appropriate for indoor positioning. The chosen models were experimented and compared based on their performances using performance metrics such as CDF curve, MAE, RSME and Euclidean distance error. Results: The literature study shows that Long Short Term Memory and Multi-layer perceptron, Gradient boosting, XG boosting and Ada boosting is suitable for models for indoor positioning. The experimentation and comparison of these models show that the overall performance of Long short-term memory network was better than multiplayer Perceptron, Gradient boosting, XG boosting and Adaboosting. Conclusions: After analysing the acquired results and taking into account the real-world scenarios to which this thesis is intended, it can be stated that the LSTM network provides the most accurate location estimation using beacons. This system can be monitored in real-time for maintenance and personnel tracking in an office environment.

High accuracy localization of objects is a crucial function for many modern applications, such as virtual and augmented reality, robotics, and self-driving cars, among others. This requires determining precise location of objects indoors, which is a challenging task. In recent years, Ultra-Wideband technology has seen increasing interest as a potential solution to this problem by the research community. This is mainly due to its innate capabilities of high update frequency and low power consumption which makes it a suitable technology for precise distance measurement and location determination. This study has aimed to answer what the state-of-the-art in the field of trilateration in Ultra-Wideband based indoor positioning systems utilizing other complementary technologies is. This was done by conducting a document survey using a Grounded theory approach for the analysis. To ensure validity and reliability of the study, the sample was collected through searching IEEE Xplore using different sets of keywords, and the potential samples was then checked using a data quality form. The analysis consisted of identifying categories and concepts in the sample. The analysis found that the Ultra-wideband based systems can achieve high positioning accuracy, but limitations such as non-line-of-sight disturbance must still be overcome for the technology to consistently achieve centimetre accuracy. These limitations are being mitigated using filtering, machine learning, and multi-sensory fusion. With these complementary technologies researchers can eliminate some of the limitations. The field does however seem to be in an exploratory stage where best practices for overcoming the current limitations are yet established.