The report details the design of a wildfire management system consisting of a swarm of electrically-powered unmanned aerial vehicles (UAVs) engaging in pre-, active- and post-fire operations. Specifically, the system monitors key parameters in wildfire detection and spread modelling, simultaneously providing a mobile communication network supporting emergency responders acting on the ground. At an altitude of 850m, necessary measurements can be taken with a three-meter ground resolution. The vehicles have a fixed-wing, single-propeller configuration, allowing a five-hour endurance. The swarm comprises twenty surveillance and five relay units, with five backup units stationed on the ground for contingency management. All necessary hardware for a swarm fits neatly into two standard twenty-foot shipping containers. After taking off from an electronically-activated deployable launch rail, the UAVs are capable of autonomous flight. The swarm intelligence is based on zig-zag flight patterns inside Voronoi sectors generated from wildfire risk maps dynamically updated by the UAVs' sensors. Ground operations are limited to monitoring the system state, as autonomous contingent behaviour is also accounted for. Assuming a maximum fire spread rate of 2.5 m/s, the system is capable of detection in 16 minutes on average. This is a competitive performance compared to existing fire detection methods such as satellites and aeroplanes. After flying, the UAVs return to the ground station and land autonomously using an arresting gear, removing the need for runways and landing gear. A financial analysis of the system reveals a cost of 750,000 USD per swarm, proving its competitiveness in price and performance compared to available market options. It is concluded that such a system is technically and financially feasible and can realistically impart a positive change to wildfire management efforts worldwide.

As climate change becomes more and more apparent, it is necessary to find sustainable methods for future aviation. The battery industry is rapidly developing, allowing for batteries with more power density which make more electric aviation possible. As the average aerobatic flight is only 30-40 minutes, it is the perfect category to test these new electric methods. By using this information, the following mission need and project statement can be formulated.

Radar technique advancements have made it possible to equip lightweight aircraft with radar systems. These systems can help determine the relative position of the world around the aircraft. Performing calculations on the incoming radar signals, it is possible to determine the locations of the ground elements in the aircraft body of reference, which can be done using Direction of Arrival Estimation (DAE) in a lateral setting, as a Side Looking Airborne Radar (SLAR). Using traditional computing techniques for image processing as well as two pre-trained image segmentation machine learning algorithms, it is possible to identify the aforementioned structural elements onto a satellite image to determine the actual position of the aircraft. As a consequence, navigation may be possible alongside Global Positioning Systems (GPS) methods, through obtaining the coordinates of the aircraft based on radar images. Experiment results show that a high accuracy identification rate is possible, based on large features, such as highways, within the radar image.