<div> <div> <div> <p>This paper introduced a near real-time acoustic unmanned aerial vehicle detection system with multiple listening nodes using machine learning models. An audio dataset was collected in person by recording the sound of an unmanned aerial vehicle flying around as well as the sound of background noises. After the data collection phase, support vector machines and convolutional neural networks were built with two features, Mel-frequency cepstral coefficients and short-time Fourier transform. Considering the near real-time environment, the features were calculated after cutting the audio stream into chunks of two, one or half seconds. There are four combinations of features and models as well as three versions per combination based on the chunk size, returning twelve models in total. To train support vector machines, the exhaustive search method was used to find the best parameter while convolutional neural networks were built by selecting the parameters manually. Four node configurations were devised to find the best way to place six listening nodes. Twelve models were run for each configuration, generating color maps to show the paths of the unmanned aerial vehicle flying along the nodes. The model of short-time Fourier transform and support vector machines showed the path most clearly with the least false negatives with 2-second chunk size. Among the four configurations, the configuration for experiment 3 showed the best results in terms of the distance of detection results on the color maps. Web-based monitoring dashboards were provided to enable users to monitor detection results. </p> </div> </div> </div>

This master thesis deals with the analysis of the current possibilities for object tracking in the image, based on which is designed a procedure for creating a system capable of tracking an object of interest. Part of this work is designing virtual reality for the needs of implementation of the tracking system, which is finally deployed and tested on a real prototype of unmanned vehicle.

<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>