Efficient Phase-Based Acoustic Tracking of Drones using a Microphone Array

Abstract

An efficient phase-based acoustic detection and tracking algorithm for drones is presented. The algorithm separately tracks the time difference of arrival (TDOA) of the incoming signal with respect to microphone pairs based on the phase in the discrete Fourier transform (DFT) bins. The direction of arrival (DOA) of the drone is determined by forming a solution curve corresponding to each TDOA, then clustering the curve intersections. The algorithm avoids the computationally expensive grid-search over DOA, so is significantly more efficient than beamforming. The proposed algorithm and the maximum likelihood (ML) processor (beamformer) are compared in simulated and real data scenarios. Using simulated data, the ML estimator is shown to agree with the Cramer-Rao lower bound (CRLB) and the proposed algorithm is shown to approach the performance of ML at higher SNR. In real data scenarios, the phase-based algorithm implemented with simple alpha-beta TDOA trackers consistently tracked the target through difficult maneuvers at short and long range, showing no degradation with respect to the beamformer. Other potential advantages include robustness against interference and ability to create phase-based spectrograms for classification.