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:
https://lup.lub.lu.se/record/007393ac-5bc2-4741-a130-4f4fc954acfe
- author
- Swärd, Johan
LU
; Elvander, Filip
LU
and Jakobsson, Andreas
LU
- organization
- publishing date
- 2018
- 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
- project
- eSSENCE@LU 4:2 - Efficient data acquisition and analyses for modern multidimensional spectroscopy"
- language
- English
- LU publication?
- yes
- id
- 007393ac-5bc2-4741-a130-4f4fc954acfe
- date added to LUP
- 2018-04-25 11:37:06
- date last changed
- 2022-03-17 07:09:54
@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}},
keywords = {{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}},
doi = {{10.1016/j.sigpro.2018.03.011}},
volume = {{150}},
year = {{2018}},
}