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

TL;DR

This paper presents a low-cost, real-time passive system for UAV detection, classification, and DOA estimation using microphone arrays, advanced signal processing, and embedded hardware, demonstrating effective preliminary results.

Contribution

It introduces a novel low-cost hardware and signal processing approach for UAV detection and localization, including array calibration and classification methods.

Findings

Effective UAV detection and localization demonstrated in preliminary experiments

Low-cost hardware implementation with embedded STM32F405RG processor

Improved accuracy through oversampling and advanced array processing techniques

Abstract

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-linear amplification and filtering of the analog acoustic signal, avoiding also the saturation effect in case where the UAV is located nearby to the microphones. Advance array processing methods are used to detect and locate the wide-band sources in the near and far-field including array calibration and energy based beamforming techniques. Moreover, oversampling techniques are adopted to increase the acquired signals accuracy and to also decrease the quantization noise. The classifier is based on the nearest neighbor rule of a normalized Power Spectral Density, the acoustic signature of the UAV spectrum in short periods of time. The low-cost, low-power and high efficiency embedded processor STM32F405RG is used for system implementation. Preliminary experimental results have shown the effectiveness of the proposed approach.