Due to the ad hoc nature of wireless acoustic sensor networks, the position of the sensor nodes is typically unknown. This contribution proposes a technique to estimate the position and orientation of the sensor nodes from the recorded speech signals. The method assumes that a node comprises a microphone array with synchronously sampled microphones rather than a single microphone, but does not require the sampling clocks of the nodes to be synchronized. From the observed audio signals, the distances between the acoustic sources and arrays, as well as the directions of arrival, are estimated. They serve as input to a non-linear least squares problem, from which both the sensor nodes’ positions and orientations, as well as the source positions, are alternatingly estimated in an iterative process. Given one set of unknowns, i.e., either the source positions or the sensor nodes’ geometry, the other set of unknowns can be computed in closed-form. The proposed approach is computationally efficient and the first one, which employs both distance and directional information for geometry calibration in a common cost function. Since both distance and direction of arrival measurements suffer from outliers, e.g., caused by strong reflections of the sound waves on the surfaces of the room, we introduce measures to deemphasize or remove unreliable measurements. Additionally, we discuss modifications of our previously proposed deep neural network-based acoustic distance estimator, to account not only for omnidirectional sources but also for directional sources. Simulation results show good positioning accuracy and compare very favorably with alternative approaches from the literature.

中文翻译：

---

利用 DoA 和距离信息的无线声学传感器网络中的几何校准

由于无线声学传感器网络的自组织性质，传感器节点的位置通常是未知的。该贡献提出了一种从记录的语音信号中估计传感器节点的位置和方向的技术。该方法假设节点包括具有同步采样的麦克风而不是单个麦克风的麦克风阵列，但不要求节点的采样时钟同步。根据观察到的音频信号，估计声源和阵列之间的距离以及到达方向。它们用作非线性最小二乘问题的输入，从中可以在迭代过程中交替估计传感器节点的位置和方向以及源位置。给定一组未知数，即 无论是源位置还是传感器节点的几何形状，另一组未知数都可以以封闭形式计算。所提出的方法计算效率高，并且是第一种方法，它在公共成本函数中同时使用距离和方向信息进行几何校准。由于到达测量的距离和方向都受到异常值的影响，例如，由声波在房间表面的强烈反射引起的，我们引入了一些措施来淡化或消除不可靠的测量。此外，我们讨论了我们之前提出的基于深度神经网络的声学距离估计器的修改，不仅要考虑全向源，还要考虑定向源。仿真结果显示出良好的定位精度，并且与文献中的替代方法相比非常有利。