Iterative Geometry Calibration from Distance Estimates for Wireless Acoustic Sensor Networks
This work addresses the problem of geometry calibration for wireless acoustic sensor networks, which is crucial for accurate acoustic source localization and tracking.
This paper proposes an iterative weighted least squares localization procedure, initialized by multidimensional scaling, to calibrate the geometry of wireless acoustic sensor networks using only distance estimates between acoustic sources and sensor nodes. It simultaneously estimates both sensor node and acoustic source positions, and simulations demonstrate the estimator's efficiency by showing it reaches the Cramer-Rao lower bound.
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.