This research aims to detect Unmanned Aerial Vehicles (UAVs) through the implementation of innovative machine-learning techniques based on pattern recognition and quantum signal processing applied to ..
This research aims to detect Unmanned Aerial Vehicles (UAVs) through the implementation of innovative machine-learning techniques based on pattern recognition and quantum signal processing applied to acoustic data. The presence of UAV is detected in audio signals through spectral descriptors. Fourier transform-based frequency analysis is used to identify anomalies, and recognize patterns to train a Support Vector Machine algorithm enabling classification of the UAV from background noise. Quantum signal processing techniques are employed to improve the accuracy of UAV detection. The analysis demonstrates a 90 \% confidence in predicting drone presence. The proposed acoustic detection method is both cost-effective and innovative, utilizing sound, inherent in natural systems, to achieve reliable results.
