With more aircraft in the air than ever before, there is a need for automating the surveillance of the airspace. It is widely known that aircraft with different intentions fly in different flight patterns. Support systems for finding different flight patterns are therefore needed. In this thesis, we investigate the possibility of detecting circular flight patterns using deep learning models. The basis for detection is ADS-B data which is continuously transmitted by aircraft containing information related to the aircraft status. Two deep learning models are constructed to solve the binary classification problem of detecting circular flight patterns. The first model is a Long Short-Term Memory (LSTM) model and utilizes techniques such as sliding window and bidirectional LSTM layers to solve the given task. The second model is a Convolutional Neural Network (CNN) and utilizes transfer learning. For the CNN model, the trajectory data is converted into image representations which are fed into a pre-trained model with a custom final dense layer. While ADS-B is openly available, finding specific flight patterns and producing a labeled data set of that pattern is hard and time-consuming. The data set is therefore expanded using other sources of data. Two additional sources of trajectory data are added to the data set; radar and simulated data. Training a model on data of a different distribution than the model is being evaluated on can be problematic and introduces a new source of error known as training-validation mismatch. One of the main goals of this thesis is to be able to quantify the size of this error to decide if using data from other sources is a viable option. The results show that the CNN model outperforms the LSTM model and achieves an accuracy of 98.2%. The results also show that there is a cost, in terms of accuracy, associated with not only training on ADS-B data. For the CNN model that cost was a 1-4% loss in accuracy depending on the training data used. The corresponding cost for the LSTM model was 2-10%.

This thesis presents how unmanned aerial vehicles (UAVs) can successfully assist in search and rescue (SAR) operations using wireless localization. The zone-grid to partition to capture/detect WiFi probe requests follows the concepts found in Search Theory Method. The UAV has attached a sensor, e.g., WiFi sniffer, to capture/detect the WiFi probes from victims or lost people’s smartphones. Applying the Random-Forest based machine learning algorithm, an estimation of the user's location is determined with a 81.8% accuracy. UAV technology has shown limitations in the navigational performance and limited flight time. Procedures to optimize these limitations are presented. Additionally, how the UAV is maneuvered during flight is analyzed, considering different SAR flight patterns and Li-Po battery consumption rates of the UAV. Results show that controlling the UAV by remote-controll detected the most probes, but it is less power efficient compared to control it autonomously.