@inproceedings{5ca8d583a0714a1c910ca949185e2f0b,
title = "Efficient Time-of-Arrival Self-Calibration using Source Implicitization",
abstract = "In this paper we revisit the Time-of-Arrival self-calibration problem. In particular we focus on imbalanced problem instances where there are significantly more sources compared to the number of receivers, which is a common configuration in real applications. Using an implicit representation, we are able to re-parameterize the sensor node self-calibration problem using only the parameters of the receiver positions. Making the source positions implicit, we show that it is possible to linearize the maximum-likelihood error around the measured distances, resulting in a Sampson-like approximation. Given four unknown receiver positions and a large number of unknown sender positions, we show that our formulation leads to algorithms for robust calibration, with significant speed-up compared to running the full optimization over all unknowns. The proposed method is tested on both synthetic and real data.",
keywords = "robust optimization, Sensor node calibration, Time-of-Arrival",
author = "Malte Larsson and Viktor Larsson and Magnus Oskarsson",
year = "2023",
doi = "10.23919/EUSIPCO58844.2023.10289933",
language = "English",
series = "European Signal Processing Conference",
publisher = "European Signal Processing Conference, EUSIPCO",
pages = "1644--1648",
booktitle = "31st European Signal Processing Conference, EUSIPCO 2023 - Proceedings",
note = "31st European Signal Processing Conference, EUSIPCO 2023 ; Conference date: 04-09-2023 Through 08-09-2023",
}