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Designing optimal sampling schemes

Swärd, Johan LU ; Elvander, Filip LU and Jakobsson, Andreas LU orcid (2017) 25th European Signal Processing Conference, EUSIPCO 2017 2017-January. p.912-916
Abstract

In this work, we propose a method for finding an optimal, non-uniform, sampling scheme for a general class of signals in which the signal measurements may be non-linear functions of the parameters to be estimated. Formulated as a convex optimization problem reminiscent of the sensor selection problem, the method determines an optimal sampling scheme given a suitable estimation bound on the parameters of interest. The formulation also allows for putting emphasis on a particular set of parameters of interest by scaling the optimization problem in such a way that the bound to be minimized becomes more sensitive to these parameters. For the case of imprecise a priori knowledge of these parameters, we present a framework for customizing the... (More)

In this work, we propose a method for finding an optimal, non-uniform, sampling scheme for a general class of signals in which the signal measurements may be non-linear functions of the parameters to be estimated. Formulated as a convex optimization problem reminiscent of the sensor selection problem, the method determines an optimal sampling scheme given a suitable estimation bound on the parameters of interest. The formulation also allows for putting emphasis on a particular set of parameters of interest by scaling the optimization problem in such a way that the bound to be minimized becomes more sensitive to these parameters. For the case of imprecise a priori knowledge of these parameters, we present a framework for customizing the sampling scheme to take such uncertainty into account. Numerical examples illustrate the efficiency of the proposed scheme.

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Please use this url to cite or link to this publication:
author
; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
25th European Signal Processing Conference, EUSIPCO 2017
volume
2017-January
article number
8081340
pages
5 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
25th European Signal Processing Conference, EUSIPCO 2017
conference location
Kos, Greece
conference dates
2017-08-28 - 2017-09-02
external identifiers
  • scopus:85041424458
ISBN
9780992862671
DOI
10.23919/EUSIPCO.2017.8081340
language
English
LU publication?
yes
id
26a4e9cc-c67b-4dbb-ab5e-64d756080c2c
date added to LUP
2018-02-16 08:01:39
date last changed
2025-04-04 14:11:38
@inproceedings{26a4e9cc-c67b-4dbb-ab5e-64d756080c2c,
  abstract     = {{<p>In this work, we propose a method for finding an optimal, non-uniform, sampling scheme for a general class of signals in which the signal measurements may be non-linear functions of the parameters to be estimated. Formulated as a convex optimization problem reminiscent of the sensor selection problem, the method determines an optimal sampling scheme given a suitable estimation bound on the parameters of interest. The formulation also allows for putting emphasis on a particular set of parameters of interest by scaling the optimization problem in such a way that the bound to be minimized becomes more sensitive to these parameters. For the case of imprecise a priori knowledge of these parameters, we present a framework for customizing the sampling scheme to take such uncertainty into account. Numerical examples illustrate the efficiency of the proposed scheme.</p>}},
  author       = {{Swärd, Johan and Elvander, Filip and Jakobsson, Andreas}},
  booktitle    = {{25th European Signal Processing Conference, EUSIPCO 2017}},
  isbn         = {{9780992862671}},
  language     = {{eng}},
  month        = {{10}},
  pages        = {{912--916}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  title        = {{Designing optimal sampling schemes}},
  url          = {{http://dx.doi.org/10.23919/EUSIPCO.2017.8081340}},
  doi          = {{10.23919/EUSIPCO.2017.8081340}},
  volume       = {{2017-January}},
  year         = {{2017}},
}