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Designing optimal sampling schemes

Swärd, Johan LU ; Elvander, Filip LU and Jakobsson, Andreas LU (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
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
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
2020-01-22 06:56:56
@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    = {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},
}