In this report the feasibility of using a dual-tag setup in an indoor positioning system was investigated. The reason for the dual-tag setup was to be able to estimate both position and orientation. The system was designed using UWB-technology, with an time of flight trilateration algorithm to calculate the position. The orientation was then estimated from the relative position between the two tags. The system was tested both with stationary tags, but also with the tags moving along two paths. These tests were conducted for different separation distance between the tags, namely 20 cm, 30 cm and 40 cm. The result was that the mean position error for stationary tags was less than 8 cm for all separations and the mean orientation error was less than 3$^\circ$ for all separations. For the moving tag tests a decrease of the error in orientation of about 30 \% could be observed for a separation of 30 and 40 cm compared to 20 cm. However this difference is small in absolute values so more tests are needed to draw any conclusion about whether 30 and 40 cm tag separation performs better than 20 cm tag separation. The performance of the system could also be increased further by optimizing the anchor placement as well as the calibration of the antenna delays of the UWB-modules.

Location Based Services (LBS) are a relatively recent multidisciplinary field which brings together many aspects of the fields of hardware design, digital signal processing (DSP), digital image processing (DIP), algorithm design in mathematics, and systematic implementation. LBS provide indirect location information from a variety of sensors and present these in an understandable and intuitive way to users by employing theories of data science and deep learning. Indoor positioning, which is one of the sub-applications of LBS, has become increasingly important with the development of sensor techniques and smart algorithms. The aim of this thesis is to explore the utilisation of indoor positioning algorithms under complex Non-Line of sight (LOS) environments in order to meet the requirements of both commercial and civil indoor localisation services. This thesis presents specific designs and implementations of solutions for indoor positioning systems from signal processing to positioning algorithms. Recently, with the advent of the protocol for the Bluetooth 4.0 technique, which is also called Bluetooth Low Energy (BLE), researchers have increasingly begun to focus on developing received signal strength (RSS) based indoor localisation systems, as BLE based indoor positioning systems boast the advantages of lower cost and easier deployment condition. At the meantime, information providers of indoor positioning systems are not limited by RSS based sensors. Accelerometer and magnetic field sensors may also being applied for providing positioning information by referring to the users' motion and orientation. With regards to this, both indoor localisation accuracy and positioning system stability can be increased by using hybrid positioning information sources in which these sensors are utilised in tandem. Whereas both RSS based sensors, such as BLE sensors, and other positioning information providers are limited by the fact that positioning information cannot be observed or acquired directly, which can be summarised into the Hidden Markov Mode (HMM). This work conducts a basic survey of indoor positioning systems, which include localisation platforms, using different hardware and different positioning algorithms based on these positioning platforms. By comparing the advantages of different hardware platforms and their corresponding algorithms, a Received Signal Strength Indicator (RSSI) based positioning technique using BLE is selected as the main carrier of the proposed positioning systems in this research. The transmission characteristics of BLE signals are then introduced, and the basic theory of indoor transmission modes is detailed. Two filters, the smooth filter and the wavelet filter are utilised to de-noise the RSSI sequence in order to increase localisation accuracy. The theory behind these two filter types is introduced, and a set of experiments are conducted to compare the performance of these filters. The utilisation of two positioning systems is then introduced. A novel, off-set centroid core localisation algorithm is proposed firstly and the second one is a modified Monte Carlo localisation (MCL) algorithm based system. The first positioning algorithm utilises BLE as a positioning information provider and is implemented with a weighted framework for increasing localisation accuracy and system stability. The MCL algorithm is tailor-made in order to locate users' position in an indoor environment using BLE and data received by sensors locating user position in an indoor environment. The key features in these systems are summarised in the following: the capacity of BLE to compute user position and achieve good adaptability in different environmental conditions, and the compatibility of implementing different information sources into these systems is very high. The contributions of this thesis are as follows: Two different filters were tailor-made for de-nosing the RSSI sequence. By applying these two filters, the localisation error caused by small scale fading is reduced significantly. In addition, the implementation for the two proposed are described. By using the proposed centroid core positioning algorithm in combination with a weighted framework, localisation inaccuracy is no greater than 5 metres under most complex indoor environmental conditions. Furthermore, MCL is modified and tailored for use with BLE and other sensor readings in order to compute user positioning in complex indoor environments. By using sensor readings from BLE beacons and other sensors, the stability and accuracy of the MCL based indoor position system is increased further.

Finding ones way in unknown and unfamiliar environments is a common task. A number of tools ranging from paper maps to location-based services have been introduced to assist human navigation. Undoubtedly, car navigation systems can be considered the most successful example of location based services that widely gained user acceptance. However the concept of car navigation is not always (perhaps rarely) suitable for pedestrian navigation. Moreover, precise localization of moving objects indoors is not possible due to the absence of an absolute positioning method such as GPS. These make accurate indoor tracking and navigation an interesting problem to explore. Many of the methods of spatial analysis popular in outdoor applications can be used indoors. In particular, generation of the indoor navigation network can be an effective solution for a) improving the navigation experience inside complex indoor structures and b) enhancing the analysis of the indoor tracking data collected with existing positioning solutions. Such building models should be based on a graph representation and consist of the number of ‘nodes’ and ‘edges’, where ‘nodes’ correspond to the central position of the room and ‘edge’ represents the medial axis of the hallway polygons, which physically connects these rooms. Similar node-links should be applied stairs and elevators to connect building floors. To generate this model, I selected the campus of University of Saskatchewan as the study area and presented a method that creates an indoor navigation network using ESRI ArcGIS products. First, the proposed method automatically extracts geometry and topology of campus buildings and computes the distances among all entities to calculate the shortest path between them. The system navigates through the University campus and it helps locating classrooms, offices, or facilities. The calculation of the route is based on the Dijkstra algorithm, but could employ any network navigation algorithm. To show the advantage of the generated network, I present results of a study conducted in conjunction with the department of Computer Science. An experiment that included 37 participants was designed to collect the tracking data on a university campus to demonstrate how the incorporation of the indoor navigation model can improve the analysis of the indoor movement data. Based on the results of the study, it can be concluded that the generated indoor network can be applied to raw positioning data in order to improve accuracy, as well as be employed as a stand-alone tool for enhancing of the route guidance on a university campus, and by extension any large indoor space consisting of individual or multiple buildings.

In navigation from one place to another, spatial knowledge helps us establish a destination and route while travelling. Therefore, sufficient spatial knowledge is a vital element in successful navigation. To build adequate spatial knowledge, various forms of spatial tools have been introduced to deliver spatial information without direct experience (maps, descriptions, pictures, etc.). An innovation developed in the 1970s and available on many handheld platforms from the early 2000s is the Global Position System (GPS) and related map and text-based navigation support systems. Contemporary technical achievements, such as GPS, have made navigation more effective, efficient, and comfortable in most outdoor environments. Because GPS delivers such accurate information, human navigation can be supported without specific spatial knowledge. Unfortunately, there is no universal and accurate navigation system for indoor environments. Since smartphones have become increasingly popular, we can more frequently and easily access various positioning services that appear to work both indoors and outdoors. The expansion of positioning services and related navigation technology have changed the nature of navigation. For example, routes to destination are progressively determined by a “system,” not the individual. Unfortunately we only have a partial and nascent notion of how such an intervention affects spatial behaviour. The practical purpose of this research is to develop a trustworthy positioning system that functions in indoor environments and identify those aspects those should be considered before deploying Indoor Positioning System (IPS), all towards the goal of maintaining affordable positioning accuracy, quality, and consistency. In the same way that GPS provides worry free directions and navigation support, an IPS would extend such opportunities to many of our built environments. Unfortunately, just as we know little about how GPS, or any real time navigation system, affects human navigation, there is little evidence suggesting how such a system (indoors or outdoors) changes how we find our way. For this reason, in addition to specifying an indoor position system, this research examines the difference in human’s spatial behaviour based on the availability of a navigation system and evaluates the impact of varying the levels of availability of such tools (not available, partially available, or full availability). This research relies on outdoor GPS, but when such systems are available indoors and meet the accuracy and reliability or GPS, the results will be generalizable to such situations.

Use of wireless network information for indoor positioning has been an area of interest since wireless networks became very popular. On the other hand, the market started to grow in variety and production volumes leading to a variety of devices with many different hardware and software combinations. In the field of indoor positioning, most of the existing technologies are dependent on additional hardware and/or infrastructure, which increases the cost and requirements for both users and providers. This thesis investigates possible methods of coupling indoor geo-fencing with access control including authentication, identification, and registration in a system. Moreover, various techniques are studied in order to improve the robustness and security of such a system. The focus of these studies is to improve the proposed system in such a way that gives it the ability to operate properly in noisy, heterogeneous, and less controlled environments where the presence of attackers is highly probable. To achieve this, a classification based geo-fencing approach using Received Signal Strength Indicator (RSSI) has been employed so that accurate geo-fencing is coupled with secure communication and computing. Experimental results show that considerable positioning accuracy has been achieved while providing high security measures for communication and transactions. Favouring diversity and generic design, the proposed implementation does not mandate users to undergo any system software modification or adding new hardware components.

In this thesis, the neural network, a powerful tool which has demonstrated its ability in many fields, is studied for the indoor localization system and channel prediction system. This thesis first proposes a received signal strength indicator (RSSI) fingerprinting-based indoor positioning system for the widely deployed WiFi environment, using deep neural networks (DNN). To reduce the computing time as well as improve the estimation accuracy, a two-step scheme is designed, employing a classification network for clustering and several regression networks for final location prediction. A new fingerprinting, which utilizes the similarity in RSSI readings of the nearby reference points (RPs) is also proposed. Real-time tests demonstrate that the proposed algorithm achieves an average distance error of 43.5 inches. Then this thesis extends the ability of the neural network to the physical layer communications by introducing a recurrent neural network (RNN) based approach for real-time channel prediction which uses the recent history channel state information (CSI) estimation for online training before prediction, to adapt to the continuously changing channel to gain a more accurate CSI prediction compared to the other conventional methods. Furthermore, the proposed method needs no additional knowledge, neither the internal properties of the channel itself nor the external features that affect the channel propagation. The proposed approach outperforms the other methods in a changing environment in the simulation test, validating it a promising method for channel prediction in wireless communications. / Graduate

In our day to day activity, imagine if you go to a museum, hospital or any kind of huge building. You need to find the best way to get into a specific depart- ment. It might be difficult to find the way even if you have the map of the building in your hand. Indoor positioning systems can be used to locate people or objects inside a building, using radio waves, signals, or other sensory information collected by a smartphone or tablet. Bluetooth Low Energy (BLE) beacons broadcast signals, and bluetooth devices, such as smartphones, can then receive these signals. BLE devices can take Received Signal Strength Indication (RSSI) information together with an algorithm to calculate the location of the user. This is a useful method for indoor environments when using Global Positioning System (GPS) is not an option [1]. In this project I tried to find a better solution for localization and navigation when GPS does not work. The focus of the project is to use communication be- tween smartphones and beacons, for guidance in inside environments, without using GPS. This thesis is about the applications I produced, which can be used for indoor localization and navigation. Using the applications, you can map any building such as university, hospital, museum, big mall etc. To map a building, you upload a map of the building and put waypoints where you placed beacons. Once mapping is done, you can log in to the web admin and put some informa- tion for each beacon. As a user, when outside a nearby mapped building, your phone can get notified (trough bluetooth), and you can download the user app, which includes the map of the building and shows your location. With the user app you can easily find your favorite places in the building and get information about place near you.

This thesis elaborates on the design process of a mobile phone based application for indoor positioning at workplaces. The aim of the application is to ease workflow management and help increase the work efficiency of individuals and teams by reducing the amount of time spent in looking and waiting for each other. In doing so, the research takes a closer look on the user’s perspective on sharing one’s location data. An attempt is made to explore users’ behavior, investigating if any privacy concerns arise out of sharing one’s indoor location data and how it effects the adoption of the service within the context of a workspace. This exploratory approach employed a number of qualitative tools in order to gather data and analyze it. In order to understand the complex context of a work environment where activities (or actions) are defined by a number of factors, actors, mediators, communication channels, etc., the research followed an activity centred approach. The resulting solution is in the form of a service that provides layers of contextual information, responding to the overall activity being performed and the smaller actions that constitute it. A prototype of this application is then taken for user testing. The test results show that the users were hesitant in sharing their location data; citing a number of speculated scenarios where this information may be used in ways that induced a sense of being spied upon. However, in the overall acceptance and adoption of the system, the context of use (the workspace) was found to play a very crucial role.

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Master of Science / The 6 GHz spectrum band (5.925 GHz – 7.125 GHz) is an important frequency range for many industries due to its high bandwidth capabilities, low latency, and ability to support high data transfer rates. Different types of services, both fixed and mobile, are utilizing the 6 GHz frequency band at present. The incumbents of this band comprise governmental and commercial entities that depend on the 6 GHz spectrum for services like transportation and public safety. The 6 GHz spectrum has also been identified for use by various wireless communication systems, including Wi-Fi, Bluetooth, and 5G. Incumbent licensed operators need to be able to access the spectrum without significant interference to operate effectively. As more wireless communication systems are developed and deployed, the demand for spectrum continues to grow. There is a need for spectrum sharing due to the scarcity of coverage-friendly low band spectrum. Indeed, 6G is expected to use spectrum sharing to a much larger extent compared to previous generations of wireless systems. This thesis provides extensive experimentation results using a commercial FML system that can be used to understand resiliency of FML receivers to interference at 6 GHz.