Localizing Unsynchronized Sensors With Unknown Sources
(2023) In IEEE Transactions on Signal Processing 71. p.641-654- Abstract
We propose a method for sensor array self-localization using a set of sources at unknown locations. The sources produce signals whose times of arrival are registered at the sensors. We look at the general case where neither the emission times of the sources nor the reference time frames of the receivers are known. Unlike previous work, our method directly recovers the array geometry, instead of first estimating the timing information. The key component is a new loss function which is insensitive to the unknown timings. We cast the problem as a minimization of a non-convex functional of the Euclidean distance matrix of microphones and sources subject to certain non-convex constraints. After convexification, we obtain a semidefinite... (More)
We propose a method for sensor array self-localization using a set of sources at unknown locations. The sources produce signals whose times of arrival are registered at the sensors. We look at the general case where neither the emission times of the sources nor the reference time frames of the receivers are known. Unlike previous work, our method directly recovers the array geometry, instead of first estimating the timing information. The key component is a new loss function which is insensitive to the unknown timings. We cast the problem as a minimization of a non-convex functional of the Euclidean distance matrix of microphones and sources subject to certain non-convex constraints. After convexification, we obtain a semidefinite relaxation which gives an approximate solution; subsequent refinement on the proposed loss via the Levenberg-Marquardt scheme gives the final locations. Our method achieves state-of-the-art performance in terms of reconstruction accuracy, speed, and the ability to work with a small number of sources and receivers. It can also handle missing measurements and exploit prior geometric and temporal knowledge, for example if either the receiver offsets or the emission times are known, or if the array contains compact subarrays with known geometry.
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- author
- Badawy, Dalia El ; Larsson, Viktor LU ; Pollefeys, Marc and Dokmanic, Ivan
- organization
- publishing date
- 2023
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- array signal processing, calibration, convex optimzation, Localization, wireless sensor networks
- in
- IEEE Transactions on Signal Processing
- volume
- 71
- pages
- 14 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- external identifiers
-
- scopus:85149388900
- ISSN
- 1053-587X
- DOI
- 10.1109/TSP.2023.3245284
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © 1991-2012 IEEE.
- id
- 1446399e-6768-4184-8c5b-13737eae905f
- date added to LUP
- 2024-01-12 14:07:29
- date last changed
- 2024-02-09 10:42:59
@article{1446399e-6768-4184-8c5b-13737eae905f,
abstract = {{<p>We propose a method for sensor array self-localization using a set of sources at unknown locations. The sources produce signals whose times of arrival are registered at the sensors. We look at the general case where neither the emission times of the sources nor the reference time frames of the receivers are known. Unlike previous work, our method directly recovers the array geometry, instead of first estimating the timing information. The key component is a new loss function which is insensitive to the unknown timings. We cast the problem as a minimization of a non-convex functional of the Euclidean distance matrix of microphones and sources subject to certain non-convex constraints. After convexification, we obtain a semidefinite relaxation which gives an approximate solution; subsequent refinement on the proposed loss via the Levenberg-Marquardt scheme gives the final locations. Our method achieves state-of-the-art performance in terms of reconstruction accuracy, speed, and the ability to work with a small number of sources and receivers. It can also handle missing measurements and exploit prior geometric and temporal knowledge, for example if either the receiver offsets or the emission times are known, or if the array contains compact subarrays with known geometry.</p>}},
author = {{Badawy, Dalia El and Larsson, Viktor and Pollefeys, Marc and Dokmanic, Ivan}},
issn = {{1053-587X}},
keywords = {{array signal processing; calibration; convex optimzation; Localization; wireless sensor networks}},
language = {{eng}},
pages = {{641--654}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
series = {{IEEE Transactions on Signal Processing}},
title = {{Localizing Unsynchronized Sensors With Unknown Sources}},
url = {{http://dx.doi.org/10.1109/TSP.2023.3245284}},
doi = {{10.1109/TSP.2023.3245284}},
volume = {{71}},
year = {{2023}},
}