Optimized TDOA-based drone localization with distributed microphones

Abstract

Accurately localizing drones in complex environments remains a significant challenge, with important implications for defense, law enforcement, and autonomous systems. This study addresses the problem of estimating drone localization in environments characterized by strong reflections and noise. We employ Time Difference of Arrival (TDOA) techniques for localization estimation and compare them with a specialized machine learning regression model. While previous works have considered neural networks for audio source localization, they often suffer from limited generalization across different environments. To address this, we propose a novel method that enhances the TDOA vector by incorporating both primary and secondary peaks of the cross-correlation, guided by the Zero Cyclic Sum condition. Additionally, we introduce optimization strategies that selectively reduce the number of TDOA inputs based on a least-squares cost function. We present a comparative analysis of TDOA-based optimization techniques with a machine learning method that utilizes the environment's reverberation fingerprint as input features for training. Experimental results demonstrate that the proposed TDOA-based method achieves a localization accuracy of 0.55±0.35 meters, showcasing its effectiveness and practical applicability in challenging acoustic environments.