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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)
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author
organization
publishing date
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:84922476705
  • scopus:84903401820
  • 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
2014-08-21 15:37:59
date last changed
2017-09-25 12:36:26
@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},
  keyword      = {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},
  volume       = {107},
  year         = {2015},
}