Efficient Time-of-Arrival Self-Calibration using Source Implicitization
(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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- author
- Larsson, Malte LU ; Larsson, Viktor LU and Oskarsson, Magnus LU
- organization
-
- Computer Vision and Machine Learning (research group)
- Mathematics (Faculty of Engineering)
- LTH Profile Area: AI and Digitalization
- Mathematical Imaging Group (research group)
- LU Profile Area: Natural and Artificial Cognition
- eSSENCE: The e-Science Collaboration
- ELLIIT: the Linköping-Lund initiative on IT and mobile communication
- publishing date
- 2023
- 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}}, }