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Efficient Time-of-Arrival Self-Calibration using Source Implicitization

Larsson, Malte LU orcid ; Larsson, Viktor LU and Oskarsson, Magnus LU orcid (2023) 31st European Signal Processing Conference, EUSIPCO 2023 In European Signal Processing Conference p.1644-1648
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... (More)

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.

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Please use this url to cite or link to this publication:
author
; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
robust optimization, Sensor node calibration, Time-of-Arrival
host publication
31st European Signal Processing Conference, EUSIPCO 2023 - Proceedings
series title
European Signal Processing Conference
pages
5 pages
publisher
European Signal Processing Conference, EUSIPCO
conference name
31st European Signal Processing Conference, EUSIPCO 2023
conference location
Helsinki, Finland
conference dates
2023-09-04 - 2023-09-08
external identifiers
  • scopus:85178366987
ISSN
2219-5491
ISBN
9789464593600
DOI
10.23919/EUSIPCO58844.2023.10289933
language
English
LU publication?
yes
id
5ca8d583-a071-4a1c-910c-a949185e2f0b
date added to LUP
2024-01-08 10:57:02
date last changed
2024-01-08 10:57:57
@inproceedings{5ca8d583-a071-4a1c-910c-a949185e2f0b,
  abstract     = {{<p>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.</p>}},
  author       = {{Larsson, Malte and Larsson, Viktor and Oskarsson, Magnus}},
  booktitle    = {{31st European Signal Processing Conference, EUSIPCO 2023 - Proceedings}},
  isbn         = {{9789464593600}},
  issn         = {{2219-5491}},
  keywords     = {{robust optimization; Sensor node calibration; Time-of-Arrival}},
  language     = {{eng}},
  pages        = {{1644--1648}},
  publisher    = {{European Signal Processing Conference, EUSIPCO}},
  series       = {{European Signal Processing Conference}},
  title        = {{Efficient Time-of-Arrival Self-Calibration using Source Implicitization}},
  url          = {{http://dx.doi.org/10.23919/EUSIPCO58844.2023.10289933}},
  doi          = {{10.23919/EUSIPCO58844.2023.10289933}},
  year         = {{2023}},
}