基于小尺寸麦克风阵列的服务机器人大规模三维声源定位方法

Long Chen, Lei Huang, Guitong Chen, Weize Sun
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引用次数: 0

摘要

传声器阵列的工作频率范围和扫描区域的大小通常受到阵列几何形状的限制。由于其可移动特性,对于服务机器人来说,在三维全局视图下实现更宽的工作频率范围需要一个更大、更密集的三维阵列,这可以通过使用带有可移动麦克风原型阵列的非同步测量波束形成来实现。然而,即使使用最先进的方法,也很难定位多个宽带源,因为在没有任何关于目标信号的先验信息的情况下,很难选择适当的工作频率。为此,本文提出了一种基于张量补全的宽带多声源定位非同步测量方法。分析了宽带信号的张量数据结构,提出了一种基于乘法器优化和张量多范数约束的交替方向方法。该算法可以通过16声道平面麦克风阵列提供具有不同语音信号源清晰的三维全局视图的高精度声音图。与基于矩阵的优化方法相比,该方法能显著降低估计源位置的均方误差。
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A Large Scale 3D Sound Source Localisation Approach Achieved via Small Size Microphone Array for Service Robots
Ahstract-The working frequency range and the scale of the scanning area of a microphone array are typically limited by the array geometry. Owing to its movable feature, for the service robots, achieving a wider working frequency range with a 3-dimension global view requires a virtually larger and denser 3-dimension array, which can be realised by using non-synchronous measurements beamforming with a movable microphone prototype array. However, even when using the state-of-the-art method, it is challenging to localise multiple broadband sources, owing to the difficulty in selecting an appropriate operating frequency without any prior information about the target signal. Therefore, this paper proposes a tensor-completion-based non-synchronous measurement method for broadband multiple-sound-source localisation. The tensor data structure of the broadband signal is analysed, and an alternating direction method based on multiplier optimisation with a tensor multi-norm constraint is proposed. This algorithm can provide a sound map with a distinct 3-dimension global view of different speech signal sources with high accuracy via a 16-channel planar microphone array. Compared with the matrix-based optimisation method, the proposed method can significantly reduce the mean square error of the estimated source location.
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