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The reliance on GPS for Unmanned Aerial Vehicle (UAV) localization limits the areas of application to places with a stable GPS signal. The emergence of deep learning in computer vision has made deep learning methods for visual UAV navigation a promising candidate for autonomous GPS denied localization. These method locate using images taken by a mounted camera on the UAV. Most works in the field evaluate localization ability in urban environments dense with artificial structures. This thesis analyses the localization ability of one such method over agricultural fields and forests in comparison to urban areas to investigate whether such systems rely on artificial structure or if they can function in a general environment. The localization technique is based on the deep feature Lucas-Kanade algorithm and uses convolutional neural network extracted feature representations of images taken by the UAV and satellite images to place the UAV within the satellite image for a position estimate. A network interpretation method is also applied to the problem to investigate whether it can help explain what causes the potential differences in localization accuracy between the areas. The investigation finds that the localization method is applicable in both forests and agricultural fields and pinpoints other factors than the prevalence of artificial structure that are more important for accurate localization. Further, a potential improvement to the algorithm is proposed that is shown to notably improve localization accuracy in certain conditions. It is based on obtaining a second position estimate by reversing the optimization direction and choosing the better of the two based on a loss function. / Obemannade luftburna fordon (UAV) är generellt beroende av GPS för autonom lokalisering vilket begränsar deras användningsområden till platser med en stabil GPS signal. Framväxten av djupinlärning inom datorseende har gjort djupinlärningsbaserade metoder för visuell UAV navigation en lovande kandidat för UAV lokalisering oberoende av GPS. De flesta vetenskapliga artiklar inom området utvärderar lokaliseringsförmågan i urbana miljöer som är fyllda med artificiella strukturer såsom hus och vägar. I den här uppsatsen analyseras lokaliseringsförmågan av en sådan metod över jordbruksområden och skog i förhållande till urbana miljöer för att undersöka om sådana system är beroende av artificiell struktur för att lokalisera korrekt. Lokaliseringsmetoden är baserad på Lukas-Kanade-algoritmen på djupa repesentationer. Konvolverande neurala nätverk tränas för att extrahera representationer av UAV- och satellitbilder som är mer passande för att bestämma förhållandet mellan kamerapositionerna med Lukas-Kanade algoritmen. En nätverkstolkningsmetod appliceras även på problemet för att undersöka huruvida det kan användas för att förklara eventuella skillnader i lokaliseringsförmåga mellan områdena. Undersökningen finner att lokaliseringsmetoden fungerar väl i jordbruksområden och skog och fastställer andra faktorer som är viktigare för välfungerande lokalisering än förekomsten av artificiella strukturer. Ytterligare föreslås en potentiell förbättring till algoritmen som visas kunna förbättra lokaliseringsnoggrannheten markant i vissa förhållanden. Förbättringen är baserad på att utvinna en andra positionsuppskattning genom att omvända optimeringsriktningen och välja den bättre av de två baserat på en förlustfunktion.

<div> The Unmanned Aerial Vehicles (UAV) technology has evolved exponentially in recent years. Smaller and less expensive devices allow a world of new applications in different areas, but as this progress can be beneficial, the use of UAVs with malicious intentions also poses a threat. UAVs can carry weapons or explosives and access restricted zones passing undetected, representing a real threat for civilians and institutions. Acoustic detection in combination with machine learning models emerges as a viable solution since, despite its limitations related with environmental noise, it has provided promising results on classifying UAV sounds, it is adaptable to multiple environments, and especially, it can be a cost-effective solution, something much needed in the counter UAV market with high projections for the coming years. The problem addressed by this project is the need for a real-world adaptable solution which can show that an array of acoustic sensors can be implemented for the detection and localization of UAVs with minimal cost and competitive performance.<br><br></div><div> In this research, a low-cost acoustic detection system that can detect, in real time, about the presence and direction of arrival of a UAV approaching a target was engineered and validated. The model developed includes an array of acoustic sensors remotely connected to a central server, which uses the sound signals to estimate the direction of arrival of the UAV. This model works with a single microphone per node which calculates the position based on the acoustic intensity change produced by the UAV, reducing the implementation costs and being able to work asynchronously. The development of the project included collecting data from UAVs flying both indoors and outdoors, and a performance analysis under realistic conditions. <br><br></div><div> The results demonstrated that the solution provides real time UAV detection and localization information to protect a target from an attacking UAV, and that it can be applied in real world scenarios. </div><div><br></div>