While vision-based localization techniques have been widely studied for small autonomous unmanned vehicles (SAUVs), sound-source localization capabilities have not been fully enabled for SAUVs. This paper presents two novel approaches for SAUVs to perform three-dimensional (3D) multi-sound-sources localization (MSSL) using only the inter-channel time difference (ICTD) signal generated by a self-rotating bi-microphone array. The proposed two approaches are based on two machine learning techniques viz., Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Random Sample Consensus (RANSAC) algorithms, respectively, whose performances were tested and compared in both simulations and experiments. The results show that both approaches are capable of correctly identifying the number of sound sources along with their 3D orientations in a reverberant environment.

虽然基于视觉的定位技术已被广泛研究用于小型自主无人车 (SAUV)，但尚未完全为 SAUV 启用声源定位能力。本文提出了两种新方法，使 SAUV 仅使用自旋转双麦克风阵列生成的通道间时间差 (ICTD) 信号来执行三维 (3D) 多声源定位 (MSSL)。所提出的两种方法基于两种机器学习技术，即基于密度的噪声应用空间聚类（DBSCAN）和随机样本共识（RANSAC）算法，它们的性能在模拟和实验中进行了测试和比较。