Multiple sound source localization is a hot issue of concern in recent years. The Single Source Zone (SSZ) based localization methods achieve good performance due to the detection and utilization of the Time-Frequency (T-F) zone where only one source is dominant. However, some T-F points consisting of components from multiple sources are also included in the detected SSZ sometimes. Once a T-F point in SSZ is contributed by multiple components, this point is defined as an outlier. The existence of outliers within the detected SSZ is usually an unavoidable problem for SSZ-based methods. To solve this problem, a multi-source localization by using offset residual weight is proposed in this paper. In this method, an assumption is developed: the direction estimated by all the T-F points within the detected SSZ has a difference along with the actual direction of sources. But this difference is much smaller than the difference between the directions estimated by the outliers along with the actual source localization. After verifying this assumption experimentally, Point Offset Residual Weight (PORW) and Source Offset Residual Weight (SORW) are proposed to reduce the influence of outliers on the localization results. Then, a composite weight is formed by combining PORW and SORW, which can effectively distinguish the outliers and desired points. After that, the outliers are removed by composite weight. Finally, a statistical histogram of DOA estimation with outliers removed is used for multi-source localization. The objective evaluation of the proposed method is conducted in various simulated environments. The results show that the proposed method achieves a better performance compared with the reference methods in sources localization.

中文翻译：

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基于偏移残差权重的多源定位

多声源定位是近年来备受关注的热点问题。由于检测和利用只有一个源占主导地位的时频 (TF) 区域，基于单源区域 (SSZ) 的定位方法实现了良好的性能。但是，有时检测到的 SSZ 中也会包含一些由多个来源的分量组成的 TF 点。一旦 SSZ 中的一个 TF 点由多个组件贡献，则该点被定义为异常值。检测到的 SSZ 中存在异常值通常是基于 SSZ 的方法不可避免的问题。为了解决这个问题，本文提出了一种利用偏移残差权重的多源定位。在这种方法中，提出了一个假设：检测到的 SSZ 内所有 TF 点估计的方向与源的实际方向存在差异。但是这种差异远小于异常值估计的方向与实际源定位之间的差异。在实验验证了这一假设后，提出了点偏移残差（PORW）和源偏移残差（SORW）来减少异常值对定位结果的影响。然后将PORW和SORW组合起来形成复合权重，可以有效区分离群点和期望点。之后，通过复合权重去除异常值。最后，去除异常值的 DOA 估计的统计直方图用于多源定位。所提出的方法的客观评估是在各种模拟环境中进行的。结果表明，与参考方法相比，所提出的方法在源定位方面取得了更好的性能。