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The present work deals with a new passive system for real-time detection,classification and direction of arrival estimator of Unmanned Aerial Vehicles(UAVs). The proposed system composed of a very low cost hardware components,comprises two different arrays of three or six-microphones, non-linearamplification and filtering of the analog acoustic signal, avoiding also thesaturation effect in case where the UAV is located nearby to the microphones.Advance array processing methods are used to detect and locate the wide-bandsources in the near and far-field including array calibration and energy basedbeamforming techniques. Moreover, oversampling techniques are adopted toincrease the acquired signals accuracy and to also decrease the quantizationnoise. The classifier is based on the nearest neighbor rule of a normalizedPower Spectral Density, the acoustic signature of the UAV spectrum in shortperiods of time. The low-cost, low-power and high efficiency embedded processorSTM32F405RG is used for system implementation. Preliminary experimental resultshave shown the effectiveness of the proposed approach.

Real-Time detection, classification and DOA estimation of Unmanned Aerial Vehicle