Iterative Geometry Calibration from Distance Estimates for Wireless Acoustic Sensor Networks

TL;DR

This paper introduces an iterative method for calibrating the geometry of wireless acoustic sensor networks using only distance estimates, improving source and sensor localization accuracy.

Contribution

It presents a novel iterative weighted least squares approach initialized by multidimensional scaling for joint source and sensor localization.

Findings

The method achieves accurate localization in simulations.

The estimator is shown to be efficient, reaching the Cramer-Rao lower bound.

The approach works solely with distance estimates, simplifying calibration.

Abstract

In this paper we present an approach to geometry calibration in wireless acoustic sensor networks, whose nodes are assumed to be equipped with a compact microphone array. The proposed approach solely works with estimates of the distances between acoustic sources and the nodes that record these sources. It consists of an iterative weighted least squares localization procedure, which is initialized by multidimensional scaling. Alongside the sensor node locations, also the positions of the acoustic sources are estimated. Furthermore, we derive the Cramer-Rao lower bound (CRLB) for source and sensor position estimation, and show by simulation that the estimator is efficient.