基于时延估计的远场被动声源定位算法

现有的基于时延估计的声源定位算法大多假定声源是近场源，而在手机等手持式声源定位设备这种小尺度传声器阵列的应用场景中，声源主要为远场源。传统的基于时延估计的声源定位算法在处理远场源时，效果不佳。为了实现在该类情景中快速而准确地定位，提出一种适用于远场源的定位算法；同时提出一种用于计算广义互相关-相位变换(generalized cross correlation phase transformation, GCC-PHAT)时延估计结果置信度的算法，置信度用于估算时差(time difference of arrival, TDOA)协方差矩阵和选用传声器对。将传声器视为节点，用置信度表示节点间的距离。将传声器对的选用问题转化为图论中的路径规划问题，即寻找经过所有节点的最长路径。MATLAB仿真实验结果表明：当声源归为远场源时，与传统Chan算法相比，本文提出的远场定位算法在准确度和精度方面都有很大优势。采用基于路径规划的传声器对筛选算法后，远场定位算法将具有优异的抗干扰能力，在低信噪比或者高混响时间等恶劣声学环境下，也具有令人满意的定位效果。

Far-field Passive Sound Source Localization Algorithm Based on Time Delay Estimation

Existing sound source localization algorithms based on time delay estimation(TDE) predominantly assume the sound source to be near-field. However, in scenarios involving small-scale microphone arrays, such as handheld localization devices like smartphones, the sound sources are primarily far-field. Traditional sound source localization algorithms based on time delay estimation perform poorly in dealing with far-field sources. In order to achieve rapid and accurate localization in such scenarios, a localization algorithm suitable for far-field sources is proposed. Additionally, a criteria is proposed to determine the reliability of time delay estimates. This reliability was used to estimate the time difference of arrival(TDOA) covariance matrix and select appropriate microphone pairs. The microphone was regarded as a node and the distance between nodes was expressed by the reliability of time delay estimates. This paper transformed the challenge of microphone pair selection as a path planning problem in graph theory, i.e., finding the longest path passing through all nodes. The results of MATLAB simulation experiments show that considering the sound source as a far-field source, the algorithm proposed in this paper exhibits significant improvements in accuracy and precision compared to the traditional Chan algorithm. It is concluded that, after the incorporation of the path planning-based microphone pair selection algorithm, the far-field localization algorithm exhibits exceptional anti-interference capabilities. This implies that even under adverse acoustic conditions, such as low signal-to-noise ratios or high reverberation times, the algorithm proposed in this paper can achieve accurate sound source localization.