UAV DETECTION SYSTEM WITH MULTIPLE ACOUSTIC NODES USING MACHINE LEARNING MODELS
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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.