liu.seSearch for publications in DiVA
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Identification with Stochastic Sampling Time Jitter
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
2008 (English)In: Automatica, ISSN 0005-1098, Vol. 44, no 3, 637-646 p.Article in journal (Refereed) Published
Abstract [en]

This work investigates how stochastic sampling jitter noise affects the result of system identification, and proposes a modification of known approaches to mitigate the effects of sampling jitter, when the jitter is unknown and not directly measurable. By just assuming conventional additive measurement noise, the analysis shows that the identified model will get a bias in the transfer function amplitude that increases for higher frequencies. A frequency domain approach with a continuous-time model allows an analysis framework for sampling jitter noise. The bias and covariance in the frequency domain model are derived. These are used in bias compensated (weighted) least squares algorithms, and by asymptotic arguments this leads to a maximum likelihood algorithm. Continuous-time output error models are used for numerical illustrations.

Place, publisher, year, edition, pages
Elsevier, 2008. Vol. 44, no 3, 637-646 p.
Keyword [en]
Non-uniform sampling, Sampling jitter, System identification, Stochastic systems, Maximum likelihood, Least squares estimation, Frequency domain, Parametric model
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-14327DOI: 10.1016/j.automatica.2007.06.018OAI: oai:DiVA.org:liu-14327DiVA: diva2:23249
Available from: 2007-03-13 Created: 2007-03-13 Last updated: 2013-07-22
In thesis
1. Non-Uniform Sampling in Statistical Signal Processing
Open this publication in new window or tab >>Non-Uniform Sampling in Statistical Signal Processing
2007 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Non-uniform sampling comes natural in many applications, due to for example imperfect sensors, mismatched clocks or event-triggered phenomena. Examples can be found in automotive industry and data communication as well as medicine and astronomy. Yet, the literature on statistical signal processing to a large extent focuses on algorithms and analysis for uniformly, or regularly, sampled data. This work focuses on Fourier analysis, system identification and decimation of non-uniformly sampled data.

In non-uniform sampling (NUS), signal amplitude and time stamps are delivered in pairs. Several methods to compute an approximate Fourier transform (AFT) have appeared in literature, and their posterior properties in terms of alias suppression and leakage have been addressed. In this thesis, the sampling times are assumed to be generated by a stochastic process, and the main idea is to use information about the stochastic sampling process to calculate a priori properties of approximate frequency transforms. These results are also used to give insight in frequency domain system identification and help with analysis of down-sampling algorithms.

The main result gives the prior distribution of several AFTs expressed in terms of the true Fourier transform and variants of the characteristic function of the sampling time distribution. The result extends leakage and alias suppression with bias and variance terms due to NUS. Based on this, decimation of non-uniformly sampled signals, using continuous-time anti-alias filters, is analyzed. The decimation is based on interpolation in different domains, and interpolation in the convolution integral proves particularly useful. The same idea is also used to investigate how stochastic unmeasurable sampling jitter noise affects the result of system identification. The result is a modification of known approaches to mitigate the bias and variance increase caused by the sampling jitter noise.

The bottom line is that, when non-uniform sampling is present, the approximate frequency transform, identified transfer function and anti-alias filter are all biased to what is expected from classical theory on uniform sampling. This work gives tools to analyze and correct for this bias.

Place, publisher, year, edition, pages
Institutionen för systemteknik, 2007. 76 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1082
Keyword
signal processing, sampling, stochastic analysis, frequency transform
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-8480 (URN)978-91-85715-49-7 (ISBN)
Public defence
2007-05-11, Visionen, Hus B, Campus Valla, Linköpings universitet, Linköping, 10:15 (English)
Opponent
Supervisors
Available from: 2007-03-13 Created: 2007-03-13 Last updated: 2009-04-26

Open Access in DiVA

No full text

Other links

Publisher's full textRelated report

Authority records BETA

Eng, FridaGustafsson, Fredrik

Search in DiVA

By author/editor
Eng, FridaGustafsson, Fredrik
By organisation
Automatic ControlThe Institute of Technology
In the same journal
Automatica
Control Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 178 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf