Advanced

Designing sampling schemes for multi-dimensional data

Swärd, Johan LU ; Elvander, Filip LU and Jakobsson, Andreas LU (2018) In Signal Processing 150. p.1-10
Abstract

In this work, we propose a method for determining a non-uniform sampling scheme for multi-dimensional signals by solving a convex optimization problem reminiscent of the sensor selection problem. The resulting sampling scheme minimizes the sum of the Cramér–Rao lower bounds for the parameters of interest, given a desired number of sampling points. The proposed framework allows for selecting an arbitrary subset of the parameters detailing the model, as well as weighing the importance of the different parameters. Also presented is a scheme for incorporating any imprecise a priori knowledge of the locations of the parameters, as well as defining estimation performance bounds for the parameters of interest. Numerical examples illustrate... (More)

In this work, we propose a method for determining a non-uniform sampling scheme for multi-dimensional signals by solving a convex optimization problem reminiscent of the sensor selection problem. The resulting sampling scheme minimizes the sum of the Cramér–Rao lower bounds for the parameters of interest, given a desired number of sampling points. The proposed framework allows for selecting an arbitrary subset of the parameters detailing the model, as well as weighing the importance of the different parameters. Also presented is a scheme for incorporating any imprecise a priori knowledge of the locations of the parameters, as well as defining estimation performance bounds for the parameters of interest. Numerical examples illustrate the efficiency of the proposed scheme. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Sampling schemes, Convex optimization, Cramér–Rao lower bound
in
Signal Processing
volume
150
pages
10 pages
publisher
Elsevier
external identifiers
  • scopus:85045936480
ISSN
0165-1684
DOI
10.1016/j.sigpro.2018.03.011
language
English
LU publication?
yes
id
007393ac-5bc2-4741-a130-4f4fc954acfe
date added to LUP
2018-04-25 11:37:06
date last changed
2019-10-15 06:36:29
@article{007393ac-5bc2-4741-a130-4f4fc954acfe,
  abstract     = {<br/>In this work, we propose a method for determining a non-uniform sampling scheme for multi-dimensional signals by solving a convex optimization problem reminiscent of the sensor selection problem. The resulting sampling scheme minimizes the sum of the Cramér–Rao lower bounds for the parameters of interest, given a desired number of sampling points. The proposed framework allows for selecting an arbitrary subset of the parameters detailing the model, as well as weighing the importance of the different parameters. Also presented is a scheme for incorporating any imprecise a priori knowledge of the locations of the parameters, as well as defining estimation performance bounds for the parameters of interest. Numerical examples illustrate the efficiency of the proposed scheme.},
  author       = {Swärd, Johan and Elvander, Filip and Jakobsson, Andreas},
  issn         = {0165-1684},
  keyword      = {Sampling schemes,Convex optimization,Cramér–Rao lower bound},
  language     = {eng},
  pages        = {1--10},
  publisher    = {Elsevier},
  series       = {Signal Processing},
  title        = {Designing sampling schemes for multi-dimensional data},
  url          = {http://dx.doi.org/10.1016/j.sigpro.2018.03.011},
  volume       = {150},
  year         = {2018},
}