The usage of Unmanned Aerial Systems (UASs), such as quadcopters and hexacopters, has steadily increased over the past few years in both recreational and commercial use. This increased availability to purchase such systems has also given rise to many safety and security concerns. A common concern is that the misuse of a UAS can cause damage to airplanes and helicopters in and around airports. Another growing concern is the use of UASs for terrorist intentions such as using the UAS as a remote controlled bomb. There is clearly a need to be able to detect the presence of unwanted UASs in restricted areas. This thesis work presents the beginning work towards a method to detect the presence of these UASs using the blade pass frequency (BPF) of the motors and rotors of a home made quadcopter. A low cost uniform linear microphone array is first used to perform a simple delay-and-sum beamformer to spatially filter out noise sources. The beamformer output is then divided into sub-bands using three bandpass filters centered on the expected location of the fundamental BPF and its 2nd and 3rd harmonics. For each sub-band, an adaptive filter called an adaptive line enhancer is used to extract and enhance the narrowband signals. The response of the adaptive filters are then used to detect the quadcopter by looking for the presence of the 2nd and 3rd harmonics of the fundamental BPF. Static tests of the quadcopter out in a field showed promising results for this method with the ability to detect up to the 3rd harmonic 90ft away and the 2nd harmonic 130 ft away. / Master of Science / The usage of Unmanned Aerial Systems (UASs), such as quadcopters and hexacopters, has steadily increased over the past few years in both recreational and commercial use. This increased availability to purchase such systems has also given rise to many safety and security concerns. A common concern is that the misuse of a UAS can cause damage to airplanes and helicopters in and around airports. Another growing concern is the use of UASs for terrorist intentions such as using the UAS as a remote controlled bomb. There is clearly a need to be able to detect the presence of unwanted UASs in restricted areas. This thesis work presents the beginning work towards a method to detect the presence of a home made quadcopter based on the sound it produces. A series of microphone are first used to remove surrounding sounds that could interfere with the quadcopter’s sound. The output of this processes is then divided into smaller sections using three filters centered on the expected location of the most important and information rich parts of the quadcopter’s sound. For each section, a final filter is used to extract and enhance the signals of interest produced by the quadcopter. The response of these filters are then used to detect whether the quadcopter is present or not. Static tests of the quadcopter out in a field showed promising results for this method with the ability to detect the quadcopter 90 to 130 ft away.

<p dir="ltr">This research explored a solution to a high accident rate in remotely operating Unmanned Aerial Vehicles (UAVs) in a complex environment; it presented a new Brain-Computer Interface (BCI) enabled supervisory control system to fuse human and machine intelligence seamlessly. This study was highly motivated by the critical need to enhance the safety and reliability of UAV operations, where accidents often stemmed from human errors during manual controls. Existing BCIs confronted the challenge of trading off a fully remote control by humans and an automated control by computers. This study met such a challenge with the proposed supervisory control system to optimize human-machine collaboration, prioritizing safety, adaptability, and precision in operation.</p><p dir="ltr">The research work included designing, training, and testing BCI and the BCI-enabled control system. It was customized to control a UAV where the user’s motion intents and cognitive states were monitored to implement hybrid human and machine controls. The DJI Tello drone was used as an intelligent machine to illustrate the application of the proposed control system and evaluate its effectiveness through two case studies. The first case study was designed to train a subject and assess the confidence level for BCI in capturing and classifying the subject’s motion intents. The second case study illustrated the application of BCI in controlling the drone to fulfill its missions.</p><p dir="ltr">The proposed supervisory control system was at the forefront of cognitive state monitoring to leverage the power of an ML model. This model was innovative compared to conventional methods in that it could capture complicated patterns within raw EEG data and make decisions to adopt an ensemble learning strategy with the XGBoost. One of the key innovations was capturing the user’s intents and interpreting these into control commands using the EmotivBCI app. Despite the headset's predefined set of detectable features, the system could train the user’s mind to generate control commands for all six degrees of freedom of adapting to the quadcopter by creatively combining and extending mental commands, particularly in the context of the Yaw rotation. This strategic manipulation of commands showcased the system's flexibility in accommodating the intricate control requirements of an automated machine.</p><p dir="ltr">Another innovation of the proposed system was its real-time adaptability. The supervisory control system continuously monitors the user's cognitive state, allowing instantaneous adjustments in response to changing conditions. This innovation ensured that the control system was responsive to the user’s intent and adept at prioritizing safety through the arbitrating mechanism when necessary.</p>