The thesis deals with aerodynamic characteristics identification from flight measurement. The topic is part of flight mechanic – handling qualities. The first theoretic part consists of three identification methods description: Error equation method, Output error method and Filter error method. Mathematical model of an airplane is defined and restricted to the motion with 3 degree of freedom. There is also introduced simulation of flight measurement for identification software validation. Practical part is focused on experiment preparation, execution and evaluation. The airplane VUT 700 Specto had been chosen to carry out flight tests. The airplane was modified to the new electric powered VUT 700e Specto after first measurement flights with combustion engine. Data record from on-board measurement unit was completed by telemetric data from autopilot and remote control system. Flight tests were carried out in stabilised mode of autopilot in symmetric flight. The results were confronted with analytical analysis results and DATCOM+ software parameter estimation.

<div>In this thesis, the design and implementation of an autonomous system that will equip a multi-rotor unmanned aerial vehicle (UAV) for visual detection and tracking of other UAVs is presented. The results from detection and tracking are used for real-time motion planning.</div><div><br></div><div>The goal is to effectively detect unwanted UAVs, track them and finally capture them with a net. Having a net that traps the UAVs and enables dragging intruders to another location is of great importance, since these could be carrying dangerous loads.</div><div><br></div><div>The project consists of three main tasks: object detection using a stereo camera, video tracking using a Kalman filter based algorithm, and lastly executing an optimal flight plan to aim a net at the detected intruder UAV. The computer vision, motion tracking and planning algorithms are implemented as ROS nodes what makes them executable on a reduced size onboard computer that is installed on the aerial vehicle.</div><div><br></div><div>Previous work related to this project consists of either a UAV detection system with computationally heavy algorithms or a tracking algorithm that does not include information about the dynamics of the UAVs. For the capture methods, previous ideas do not consider autonomous decisions or an optimized method to guarantee capture. In this thesis, these three aspects are considered to develop a simple solution that can be mounted on any commercially available UAV.</div>

With improvements to miniaturization technologies, the ratio between operators required per UAV has become increasingly smaller at the cost of increased workload. Workload is an important factor to consider when designing the multi-UAV systems of tomorrow as too much workload may decrease an operator's performance. This study proposes the use of text to speech combined with an emphasis on a single screen design as a way of improving situational awareness and perceived workload. A controlled experiment consisting of 18 participants was conducted inside a simulator. Their situational awareness and perceived workload was measured using SAGAT and NASA-TLX respectively. The results show that the use of text to speech lead to a decrease in situational awareness for all elements inside the graphical user interface that were not directly handled by a text to speech event. All of the NASA-TLX measurements showed an improvement in perceived workload except for physical demand. Overall an improvement of perceived workload was observed when text to speech was in use.

Unmanned aerial vehicle (UAV) is an expanding interest in numerous industries for various applications. Increasing development of UAVs is happening worldwide, where various sensor attachments and functions are being added. The multi-function UAV can be used within areas where they have not been managed before. Because of their accessibility, cheap purchase, and easy-to-use, they replace expensive systems such as helicopters- and airplane-surveillance. UAV are also being applied into surveillance, combing object detection to video-surveillance and mobility to finding an object from the air without interfering with vehicles or humans ground. In this thesis, we solve the problem of using UAV on autonomous sites, finding an object and critical situation, support autonomous site operators with an extra safety layer from UAVs camera. After finding an object on such a site, uses GPS-coordinates from the UAV to see and place the detected object on the site onto a gridmap, leaving a coordinate-map to the operator to see where the objects are and see if the critical situation can occur. Directly under the object detection, reporting critical situations can be done because of safety-distance-circle leaving warnings if objects come to close to each other. However, the system itself only supports the operator with extra safety and warnings, leaving the operator with the choice of pressing emergency stop or not. Object detection uses You only look once (YOLO) as main object detection Neural Network (NN), mixed with edge-detection for gaining accuracy during bird-eye-views and motion-detection for supporting finding all object that is moving on-site, even if UAV cannot find all the objects on site. Result proofs that the UAV-surveillance on autonomous site is an excellent way to add extra safety on-site if the operator is out of focus or finding objects on-site before startup since the operator can fly the UAV around the site, leaving an extra-safety-layer of finding humans on-site before startup. Also, moving the UAV to a specific position, where extra safety is needed, informing the operator to limit autonomous vehicles speed around that area because of humans operation on site. The use of single object detection limits the effects but gathered object detection methods lead to a promising result while printing those objects onto a global positions system (GPS) map has proposed a new field to study. It leaves the operator with a viewable interface outside of object detection libraries.

Since many buildings in Canada were built prior to the advent of national and provincial energy codes and standards, quantifying building envelope thermal performance in existing buildings is an important step in identifying retrofit opportunities. Due to the lack of building codes or standards for existing buildings in Canada, development of a rapid and robust quantitative approach to evaluate and rank buildings for vertical envelope retrofits is required. Hence, this dissertation sought to develop quantitative approaches to evaluate existing building envelope thermal performance in Canada and beyond. Following current professional practices, in Chapter 1, a comprehensive study was conducted on 49 campus buildings at the University of Victoria (UVic) to evaluate potential energy savings from vertical envelope retrofits, and to further validate those savings through more detailed energy models and parametric analyses for a subset of buildings. To this end, the thermal performance of a building envelope was quantified based on its heat loss coefficient (UA), obtained from multiplying its surface area (A) by its thermal transmittance (U-value). Heat loss calculations were used as a metric to inform envelope rehabilitation prioritization, while considering other data such as age and physical condition in parallel. Archetype energy models for selected buildings were used to evaluate the impacts of envelope retrofits on energy and GHG savings. The outcomes of this study allowed the University to weigh the benefits of improved energy performance from envelope retrofits against associated capital cost expenditures. Also, the implemented methodology and studied parameters unveiled a new horizon in evaluating the thermal performance of existing building envelopes in Canada, where a building code for existing buildings has not yet been established. Considering the economic findings of the envelope retrofits studied, it was concluded that in the absence of an existing building energy code, the University would likely require additional incentives, such as higher utility costs, higher carbon taxes, or qualifying for utility incentive programs to justify improving existing building envelope performance on the basis of energy only. The strength of the proposed methodology in Chapter 1 was in its balance of effort and ultimate decision-making utility, where reasonable thermal bridging approximations based on simulation models for existing buildings can yield data accurate enough to inform a ranking exercise on a large breadth of subject buildings. However, since numerical models do not consider degradation of building materials, real moisture content, and errors associated with manufacturing and installation, actual building envelope thermal performance differs from 3D simulation models. To study this limitation, in-situ thermal assessments of building envelopes were performed to quantify their actual thermal performances. To this end, Chapters 2 to 4 of this dissertation attempted to determine the viability of an external infrared thermography (IRT) survey technique for quantification of heat losses through the opaque building envelope, and also explores its potential application in identifying and comparing sources of air leakage. The experiments were performed on wood-framed wall assemblies commonly used in Canada due to growing interest among designers, builders, and governments to encourage the use of wood as a building material. In these studies, (Chapter 2 to Chapter 4), thermal transmittances (U-values) of wall assemblies were estimated with external IRT and compared with 3D computer simulations. Furthermore, the impact of the accuracy of U-values estimated with IRT on the deviation of energy simulation outputs with metered data was examined. Finally, a novel relative quantitative infrared index (IRI) was proposed as a means to facilitate rapid evaluation and subsequent ranking of building envelope thermal performance. From the experiments in Chapters 2 & 3, it was found that the U-values obtained with IRT were comparable with simulated values suggesting IRT can be a reliable tool for estimating the thermal performance of wood-framed wall assemblies. Results also demonstrated that thermal imaging artefacts including nonlinear characteristics of infrared (IR) camera focal array, a.k.a. non-uniformity corrections (NUC) and vignetting could have a substantial influence on the accuracy of results, in particular energy model outputs. This limitation was resolved by introducing a practical approach where thermal images were taken from different incident angle. Overall, IRI was found to be a reliable metric for relative quantitative comparison of building envelope thermal performance regardless of boundary conditions. Moreover, outcomes of the IRT air leakage study in Chapter 4 indicated that combined qualitative and quantitative IRT approaches could potentially be implemented by practitioners to identify sources of air leakage and thermal bridges in buildings and compare their relative severity. Since blower door testing is gradually being introduced as a building code requirement to measure building envelope airtightness in an increasing number of Canadian jurisdictions, performing IRT simultaneously is potentially valuable exercise in this context. Ultimately, the methodologies outlined in Chapters 2 to 4 can help decision-makers to characterize building envelope retrofits from a performance perspective, and potentially serve as a basis for governments to develop policies to improve existing building energy performance. The methodologies in Chapters 2 to 4 prompted opportunities to utilize the emergent technology of small unmanned aerial vehicles (UAVs) equipped with an infrared camera for quick thermal assessments of building envelopes. The last chapter of this dissertation, Chapter 5, outlines advantages and limitations of aerial IRT (UAV-IRT) surveys compared to conventional stationary IRT. Furthermore, a set of best practices for UAV-IRT were presented to minimize dynamic measurement uncertainty. It was concluded that with the current IR camera technology, aerial surveys for quantitative thermal assessment of building envelope are not as accurate as with conventional infrared thermography; further investigations by manufacturers and researchers are recommended. / Graduate

There is an ever growing need for environmentally sustainable alternatives in today's society due to the looming threat of greenhouse gasses. One field where the need for new environmentally friendly solutions is needed is the aviation industry. The problem the industry is facing is due to the weight and space constraints that exist in aerial vehicles. In this bachelor project a solution for unmanned drones is proposed where it is powered by a hybrid solution consisting of batteries working together with fuel cells. The batteries compliment each other where the fuel cell is a lightweight energy source while the battery is used to combat the changing power demand. This project was done in collaboration with the Green Raven project to evaluate the optimal setup to power the energy system for an hour. The work was done theoretically in Matlab and Simulink to find the optimal system. From these simulations, data was collected to calculate the optimal configuration between batteries and amount of hydrogen stored in the Hydrogen tank. It was concluded that the best option to store the hydrogen was in a 2 liter tank at 300 bar together with 2 additional batteries with the capacity of 4000 mAh. This setup was concluded as the best option as it used up all hydrogen and landed with less charge in the battery than at the start point. / I takt med den globala uppvärmningen så växer behovet av klimatmedvetna hållbara lösningar. Ett område i stort behov av innovation är flygindustrin som länge varit en av de största klimatbovarna. Flygindustrin stora problem är att dess fordon både har begränsad volym och vikt. I detta kandidatexamensarbete kommer vi diskutera en hybridlösning där obemannade drönare drivs av en hybridlösning där batterier tillsammans med bränsleceller driver drönaren. Batterierna och bränslecellerna komplimenterar varandra då bränslecellerna är är lättviktiga och tillför en stabil produktion av ström till drönaren medan batterierna agerar komplement och hjälper till när det behövs extra kraft. Projektet som i samarbete med The Green Raven project utfördes för att utvärdera det optimala systemet för att förse drönaren nog med kraft i en timme. Projektet har utförts teoretiskt i Matlab och Simulink för att hitta den optimala balansen mellan batterier och bränsleceller. Från dessa simuleringar samlades data in för att optimera konfigurationen mellan bränslecellerna och batterierna. Från resultaten drogs slutsatsen att 2 batterier med en kapacitet på 4000 mAh som tillsammans med vätgas som förvarades i en 2 liter tank med ett tryck på 300 bar var den bästa konfigurationen. Denna lösning ansågs som den bästa då all vätgas förbrukades under simulation och att batteriet vid stopp hade en lägre laddning än vid flygstart.

<p>This thesis explores the usage of machine learning (ML) algorithms and software-defined radio (SDR) hardware for the detection of signal jamming and message injection cyberattacks against unmanned aerial vehicle (UAV) wireless communications. In the first work presented in this thesis, a real-time ML solution for classifying four types of jamming attacks is proposed for implementation with a UAV using an onboard Raspberry Pi computer and HackRF One SDR. Also presented in this thesis is a multioutput multiclass convolutional neural network (CNN) model implemented for the purpose of identifying the direction in which a jamming sample is received from, in addition to detecting and classifying the jamming type. Such jamming types studied herein are barrage, single-tone, successive-pulse, and protocol-aware jamming. The findings of this chapter forms the basis of a reinforcement learning (RL) approach for UAV flightpath modification as the next stage of this research. The final work included in this thesis presents a ML solution for the binary classification of three different message injection attacks against ADS-B communication systems, namely path modification, velocity drift and ghost aircraft injection attacks. The collective results of these individual works demonstrate the viability of artificial-intelligence (AI) based solutions for cybersecurity applications with respect to UAV communications.</p>

Target tracking is an important part of video surveillance from a UAV. Tracking a target in an urban environment can be difficult because of the number of occlusions present in the environment. If multiple UAVs are used to track a target and the target behavior is learned autonomously by the UAV then the task may become easier. This thesis explores the hypothesis that an existing cooperative control algorithm can be enhanced by a language modeling algorithm to improve over time the target tracking performance of one or more ground targets in a dense urban environment. Observations of target behavior are reported to the Sequence Memoizer which uses the observations to create a belief model of future target positions. This belief model is combined with a kinematic belief model and then used in a cooperative auction algorithm for UAV path planning. The results for tracking a single target using the combined belief model outperform other belief models and improve over the duration of the mission. Results from tracking multiple targets indicate that algorithmic enhancements may be needed to find equivalent success. Future target tracking algorithms should involve machine learning to enhance tracking performance.

<p>In this work, machine learning (ML) modeling is proposed for the detection and classification of global positioning system (GPS) spoofing in unmanned aerial vehicles (UAVs). Three testing scenarios are implemented in an outdoor yet controlled setup to investigate static and dynamic attacks. In these scenarios, authentic sets of GPS signal features are collected, followed by other sets obtained while the UAV is under spoofing attacks launched with a software-defined radio (SDR) transceiver module. All sets are standardized, analyzed for correlation, and reduced according to feature importance prior to their exploitation in training, validating, and testing different multiclass ML classifiers. Two schemes for the dataset are proposed, location-dependent and location-independent datasets. The location-dependent dataset keeps the location specific features which are latitude, longitude, and altitude. On the other hand, the location-independent dataset excludes these features. The resulting performance evaluation of these classifiers shows a detection rate (DR), misdetection rate (MDR), and false alarm rate (FAR) better than 92%, 13%, and 4%, respectively, together with a sub-millisecond detection time. Hence, the proposed modeling facilitates accurate real-time GPS spoofing detection and classification for UAV applications.</p> <p><br></p> <p>Then, a three-class ML model is implemented on a UAV with a Raspberry Pi processor for classifying the two GPS spoofing attacks (i.e., static, dynamic) in real-time. First, several models are developed and tested utilizing the prepared dataset. Models evaluation is carried out using the DR, F-score, FAR, and MDR, which all showed an acceptable performance. Then, the optimum model is loaded to the onboard processor and tested for real-time detection and classification. Location-dependent applications, such as fixed-route public transportation, are expected to benefit from the methodology presented herein as the longitude, latitude, and altitude features are characterized in the implemented model.</p>

Obstacle avoidance is a well researched area, however most of the works only consider a 2D environment. Drones can move in three dimensions. It is therefore of interest to develop a system that ensures safe flight in these three dimensions. Obstacle avoidance is of highest importance for drones if they are intended to work autonomously and around humans, since drones are often fragile and have fast moving propellers that can hurt humans. This project is based on the obstacle restriction algorithm in 3D, and uses OctoMap to conveniently use the sensor data from multiple sensors simultaneously and to deal with their limited field of view. The results show that the system is able to avoid obstacles in 3D. / Hinderundvikande är ett utforskat område, dock för det mesta har forskningen fokuserat på 2D-miljöer. Eftersom drönare kan röra sig i tre dimensioner är det intressant att utveckla ett system som garanterar säker rörelse i 3D. Hinderundvikande är viktigt för drönare om de ska arbeta autonomt runt människor, eftersom drönare ofta är ömtåliga och har snabba propellrar som kan skada människor. Det här projektet är baserat på Hinderrestriktionsmetoden (ORM), och använder OctoMap för att använda information från många sensorer samtidigt och för att hantera deras begränsade synfält. Resultatet visar att systemet kan undvika hinder i 3D.