Distributed Self-Localization for Acoustic Transceiver Networks

Wang, X; Hu, De

doi:10.1109/lsp.2023.3274414

Cited by 2 publications

(1 citation statement)

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“…Another area of study involves using implicit representations of source positions in ToA-based self-calibration problems [ 23 ]. Similar to the aforementioned passive self-localization and synchronization, TDoA and DoA measurements have been jointly exploited in the self-localization of acoustic transceiver networks, such as in [ 24 ], and an attempt to split the centralized two-step cost function for geometry calibration of the acoustic transceiver network into local cost functions was presented in [ 25 ] using distributed Newton optimization. Note that the deployment of the latter approaches is, however, restricted to transceiver nodes only, i.e., each node of the network needs to contain both the microphone array and the loudspeaker.…”

Section: Introductionmentioning

confidence: 99%

Reverberation-Robust Self-Calibration and Synchronization of Distributed Microphone Arrays by Mitigating Heteroscedasticity and Outlier Occurrence in TDoA Measurements

Woźniak,

Kowalczyk

2023

Sensors

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The network of distributed microphone arrays is usually established in an ad hoc manner; hence, network parameters such as the mutual positioning and rotation of the arrays, positions of sources, and synchronization of their recording onset times are initially unknown. In this article, we consider the problem of passively jointly self-calibrating and synchronizing distributed arrays in reverberant rooms. We use a typical two-step approach where, initially, the relative geometry of the network is estimated using Direction of Arrival (DoA) measurements. Subsequently, the absolute scale and synchronization parameters are estimated using Time Difference of Arrival (TDoA) measurements. This article presents methods to improve the robustness and accuracy of estimation of the absolute geometric scaling and synchronization parameters in reverberant conditions, in which TDoA measurements do not follow a normal distribution; furthermore, outliers often occur. To remedy these issues, we propose a Weighted Least Squares (WLS) estimator and schema for weighting the TDoA measurements to increase the estimation accuracy from heteroscedastic TDoA measurements. In addition, we propose an iterative reweighing algorithm with a binary weight to detect and reject TDoA outliers, which exploits the residuals of the parametric model in the least absolute value minimization. A numerical evaluation shows significant improvements in the proposed method over the state of the art in terms of the relative scaling error and mean absolute value of the synchronization parameters.

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Section: Introductionmentioning

confidence: 99%