TOA sensor network self-calibration for receiver and transmitter spaces with difference in dimension
Burgess, Simon LU ; Kuang, Yubin LU and Åström, Karl LU
(2015)
In Signal Processing
107(Online 11 June 2014).
p.33-42
- Abstract
- We study and solve the previously unstudied problem of finding both transmitter and receiver positions using only time of arrival (TOA) measurements when there is a difference in dimensionality between the affine subspaces spanned by receivers and transmitters. Anchor-free TOA network calibration has uses both in radio, radio strength and sound applications, such as calibrating ad hoc microphone arrays. Using linear techniques and requiring only minimal number of receivers and transmitters, an algorithm is constructed for general dimension p for the lower dimensional subspace. Degenerate cases are determined and partially characterized as when receivers or transmitters inhabit a lower dimensional affine subspace than was given as input.... (More)
- We study and solve the previously unstudied problem of finding both transmitter and receiver positions using only time of arrival (TOA) measurements when there is a difference in dimensionality between the affine subspaces spanned by receivers and transmitters. Anchor-free TOA network calibration has uses both in radio, radio strength and sound applications, such as calibrating ad hoc microphone arrays. Using linear techniques and requiring only minimal number of receivers and transmitters, an algorithm is constructed for general dimension p for the lower dimensional subspace. Degenerate cases are determined and partially characterized as when receivers or transmitters inhabit a lower dimensional affine subspace than was given as input. The algorithm is further extended to overdetermined cases in a straightforward manner. Utilizing the minimal solver, an algorithm using the Random Sample Consensus (RANSAC) paradigm has been constructed to simultaneously solve the calibration problem and remove severe outliers, a common problem in TOA applications. Simulated experiments show good performance for the minimal solver and the RANSAC-like algorithm under noisy measurements. Two indoor environment experiments using microphones and speakers give a RMSE of 2.35 cm and 3.95 cm on receiver and transmitter positions compared to computer vision reconstructions. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/4589481
- author
- Burgess, Simon
LU
; Kuang, Yubin
LU
and Åström, Karl
LU
- organization
- publishing date
- 2015
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- TOA, Array calibration, Minimal problem, Ad hoc microphone arrays
- in
- Signal Processing
- volume
- 107
- issue
- Online 11 June 2014
- pages
- 33 - 42
- publisher
- Elsevier
- external identifiers
-
- wos:000347759500004
- scopus:85027943933
- ISSN
- 0165-1684
- DOI
- 10.1016/j.sigpro.2014.05.034
- language
- English
- LU publication?
- yes
- id
- d90f6a6a-564a-4a8b-84f3-f7615bd4957e (old id 4589481)
- date added to LUP
- 2016-04-01 12:54:44
- date last changed
- 2022-02-19 01:43:56
@article{d90f6a6a-564a-4a8b-84f3-f7615bd4957e,
abstract = {{We study and solve the previously unstudied problem of finding both transmitter and receiver positions using only time of arrival (TOA) measurements when there is a difference in dimensionality between the affine subspaces spanned by receivers and transmitters. Anchor-free TOA network calibration has uses both in radio, radio strength and sound applications, such as calibrating ad hoc microphone arrays. Using linear techniques and requiring only minimal number of receivers and transmitters, an algorithm is constructed for general dimension p for the lower dimensional subspace. Degenerate cases are determined and partially characterized as when receivers or transmitters inhabit a lower dimensional affine subspace than was given as input. The algorithm is further extended to overdetermined cases in a straightforward manner. Utilizing the minimal solver, an algorithm using the Random Sample Consensus (RANSAC) paradigm has been constructed to simultaneously solve the calibration problem and remove severe outliers, a common problem in TOA applications. Simulated experiments show good performance for the minimal solver and the RANSAC-like algorithm under noisy measurements. Two indoor environment experiments using microphones and speakers give a RMSE of 2.35 cm and 3.95 cm on receiver and transmitter positions compared to computer vision reconstructions.}},
author = {{Burgess, Simon and Kuang, Yubin and Åström, Karl}},
issn = {{0165-1684}},
keywords = {{TOA; Array calibration; Minimal problem; Ad hoc microphone arrays}},
language = {{eng}},
number = {{Online 11 June 2014}},
pages = {{33--42}},
publisher = {{Elsevier}},
series = {{Signal Processing}},
title = {{TOA sensor network self-calibration for receiver and transmitter spaces with difference in dimension}},
url = {{http://dx.doi.org/10.1016/j.sigpro.2014.05.034}},
doi = {{10.1016/j.sigpro.2014.05.034}},
volume = {{107}},
year = {{2015}},
}