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Multi-Source Localization and Data Association for Time-Difference of Arrival Measurements

Flood, Gabrielle LU orcid and Elvander, Filip LU (2024) 32nd European Signal Processing Conference, EUSIPCO 2024 p.111-115
Abstract

In this work, we consider the problem of localizing multiple signal sources based on time-difference of arrival (TDOA) measurements. In the blind setting, in which the source signals are not known, the localization task is challenging due to the data association problem. That is, it is not known which of the TDOA measurements correspond to the same source. Herein, we propose to perform joint localization and data association by means of an optimal transport formulation. The method operates by finding optimal groupings of TDOA measurements and associating these with candidate source locations. To allow for computationally feasible localization in three-dimensional space, an efficient set of candidate locations is constructed using a... (More)

In this work, we consider the problem of localizing multiple signal sources based on time-difference of arrival (TDOA) measurements. In the blind setting, in which the source signals are not known, the localization task is challenging due to the data association problem. That is, it is not known which of the TDOA measurements correspond to the same source. Herein, we propose to perform joint localization and data association by means of an optimal transport formulation. The method operates by finding optimal groupings of TDOA measurements and associating these with candidate source locations. To allow for computationally feasible localization in three-dimensional space, an efficient set of candidate locations is constructed using a minimal multilateration solver based on minimal sets of receiver pairs. In numerical simulations, we demonstrate that the proposed method is robust both to measurement noise and TDOA detection errors. Furthermore, it is shown that the data association provided by the proposed method allows for statistically efficient estimates of the source locations.

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author
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organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
data association, localization, multilateration, optimal transport, time-difference of arrival
host publication
32nd European Signal Processing Conference, EUSIPCO 2024 - Proceedings
pages
5 pages
publisher
European Signal Processing Conference, EUSIPCO
conference name
32nd European Signal Processing Conference, EUSIPCO 2024
conference location
Lyon, France
conference dates
2024-08-26 - 2024-08-30
external identifiers
  • scopus:85208440758
ISBN
9789464593617
DOI
10.23919/eusipco63174.2024.10715317
language
English
LU publication?
yes
id
febc304f-397b-40bc-85ac-1c5b81a940ba
date added to LUP
2025-02-18 10:49:27
date last changed
2025-04-04 15:07:26
@inproceedings{febc304f-397b-40bc-85ac-1c5b81a940ba,
  abstract     = {{<p>In this work, we consider the problem of localizing multiple signal sources based on time-difference of arrival (TDOA) measurements. In the blind setting, in which the source signals are not known, the localization task is challenging due to the data association problem. That is, it is not known which of the TDOA measurements correspond to the same source. Herein, we propose to perform joint localization and data association by means of an optimal transport formulation. The method operates by finding optimal groupings of TDOA measurements and associating these with candidate source locations. To allow for computationally feasible localization in three-dimensional space, an efficient set of candidate locations is constructed using a minimal multilateration solver based on minimal sets of receiver pairs. In numerical simulations, we demonstrate that the proposed method is robust both to measurement noise and TDOA detection errors. Furthermore, it is shown that the data association provided by the proposed method allows for statistically efficient estimates of the source locations.</p>}},
  author       = {{Flood, Gabrielle and Elvander, Filip}},
  booktitle    = {{32nd European Signal Processing Conference, EUSIPCO 2024 - Proceedings}},
  isbn         = {{9789464593617}},
  keywords     = {{data association; localization; multilateration; optimal transport; time-difference of arrival}},
  language     = {{eng}},
  pages        = {{111--115}},
  publisher    = {{European Signal Processing Conference, EUSIPCO}},
  title        = {{Multi-Source Localization and Data Association for Time-Difference of Arrival Measurements}},
  url          = {{http://dx.doi.org/10.23919/eusipco63174.2024.10715317}},
  doi          = {{10.23919/eusipco63174.2024.10715317}},
  year         = {{2024}},
}