Sound Source Localization for Unmanned Aerial Vehicles in Low Signal-to-Noise Ratio Environments

In recent years, with the continuous development and popularization of unmanned aerial vehicle (UAVs) technology, the surge in the number of UAVs has led to an increasingly serious problem of illegal flights. Traditional acoustic-based UAV localization techniques have limited ability to extract short-time and long-time signal features, and have poor localization performance in low signal-to-noise ratio environments. For this reason, in this paper, we propose a deep learning-based UAV localization technique in low signal-to-noise ratio environments. Specifically, on the one hand, we propose a multiple signal classification (MUSIC) pseudo-spectral normalized mean processing technique to improve the direction of arrival (DOA) performance of a traditional broadband MUSIC algorithm. On the other hand, we design a DOA estimation algorithm for UAV sound sources based on a time delay estimation neural network, which solves the problem of limited DOA resolution and the poor performance of traditional time delay estimation algorithms under low signal-to-noise ratio conditions. We verify the feasibility of the proposed method through simulation experiments and experiments in real scenarios. The experimental results show that our proposed method can locate the approximate flight path of a UAV within 20 m in a real scenario with a signal-to-noise ratio of −8 dB.