<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>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/14502759 |
| Date | 06 May 2021 |
| Creators | Facundo Ramiro Esquivel Fagiani (10716747) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/UAV_DETECTION_AND_LOCALIZATION_SYSTEM_USING_AN_INTERCONNECTED_ARRAY_OF_ACOUSTIC_SENSORS_AND_MACHINE_LEARNING_ALGORITHMS/14502759 |
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