Minimum variance spectral estimation-based time frequency analysis for nonstationary time-series

S. C. Chan; Z. G. Zhang; K. M. Tsui

doi:10.1109/iscas.2007.378026

Minimum variance spectral estimation-based time frequency analysis for nonstationary time-series

S. C. Chan^*
, Z. G. Zhang
, K. M. Tsui

^*Corresponding author for this work

The University of Hong Kong

Research output: Contribution to journal › Conference article › peer-review

2 Scopus citations

Abstract

This paper introduces two new time-frequency analysis methods originated from the minimum variance spectral estimation (MVSE) for nonstationary time-series. First, a windowed MVSE (WMVSE) extends the conventional MVSE by windowing the observation data to obtain a timefrequency distribution for the time-series. Moreover, the window lengths are selected adaptively by the intersection of confidence intervals (ICI) rule to improve the time-frequency resolution. Secondly, a new recursive MVSE (RMVSE) is developed to process the input samples recursively at a lower arithmetic complexity for online time-frequency analysis. Simulation results show that the proposed WMVSE with adaptive windows offers better frequency resolutions than the Fourier-transformed-based time-frequency distributions, and the RMVSE has a good performance when tracking sinusoidal signals.

Original language	English
Article number	4253013
Pages (from-to)	1815-1818
Number of pages	4
Journal	Proceedings - IEEE International Symposium on Circuits and Systems
DOIs	https://doi.org/10.1109/iscas.2007.378026
State	Published - 2007
Externally published	Yes
Event	2007 IEEE International Symposium on Circuits and Systems, ISCAS 2007 - New Orleans, LA, United States Duration: 27 May 2007 → 30 May 2007

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title = "Minimum variance spectral estimation-based time frequency analysis for nonstationary time-series",

abstract = "This paper introduces two new time-frequency analysis methods originated from the minimum variance spectral estimation (MVSE) for nonstationary time-series. First, a windowed MVSE (WMVSE) extends the conventional MVSE by windowing the observation data to obtain a timefrequency distribution for the time-series. Moreover, the window lengths are selected adaptively by the intersection of confidence intervals (ICI) rule to improve the time-frequency resolution. Secondly, a new recursive MVSE (RMVSE) is developed to process the input samples recursively at a lower arithmetic complexity for online time-frequency analysis. Simulation results show that the proposed WMVSE with adaptive windows offers better frequency resolutions than the Fourier-transformed-based time-frequency distributions, and the RMVSE has a good performance when tracking sinusoidal signals.",

author = "Chan, \{S. C.\} and Zhang, \{Z. G.\} and Tsui, \{K. M.\}",

year = "2007",

doi = "10.1109/iscas.2007.378026",

language = "英语",

pages = "1815--1818",

journal = "Proceedings - IEEE International Symposium on Circuits and Systems",

issn = "0271-4310",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "2007 IEEE International Symposium on Circuits and Systems, ISCAS 2007 ; Conference date: 27-05-2007 Through 30-05-2007",

}

TY - JOUR

T1 - Minimum variance spectral estimation-based time frequency analysis for nonstationary time-series

AU - Chan, S. C.

AU - Zhang, Z. G.

AU - Tsui, K. M.

PY - 2007

Y1 - 2007

N2 - This paper introduces two new time-frequency analysis methods originated from the minimum variance spectral estimation (MVSE) for nonstationary time-series. First, a windowed MVSE (WMVSE) extends the conventional MVSE by windowing the observation data to obtain a timefrequency distribution for the time-series. Moreover, the window lengths are selected adaptively by the intersection of confidence intervals (ICI) rule to improve the time-frequency resolution. Secondly, a new recursive MVSE (RMVSE) is developed to process the input samples recursively at a lower arithmetic complexity for online time-frequency analysis. Simulation results show that the proposed WMVSE with adaptive windows offers better frequency resolutions than the Fourier-transformed-based time-frequency distributions, and the RMVSE has a good performance when tracking sinusoidal signals.

AB - This paper introduces two new time-frequency analysis methods originated from the minimum variance spectral estimation (MVSE) for nonstationary time-series. First, a windowed MVSE (WMVSE) extends the conventional MVSE by windowing the observation data to obtain a timefrequency distribution for the time-series. Moreover, the window lengths are selected adaptively by the intersection of confidence intervals (ICI) rule to improve the time-frequency resolution. Secondly, a new recursive MVSE (RMVSE) is developed to process the input samples recursively at a lower arithmetic complexity for online time-frequency analysis. Simulation results show that the proposed WMVSE with adaptive windows offers better frequency resolutions than the Fourier-transformed-based time-frequency distributions, and the RMVSE has a good performance when tracking sinusoidal signals.

UR - https://www.scopus.com/pages/publications/34548856260

U2 - 10.1109/iscas.2007.378026

DO - 10.1109/iscas.2007.378026

M3 - 会议文章

AN - SCOPUS:34548856260

SN - 0271-4310

SP - 1815

EP - 1818

JO - Proceedings - IEEE International Symposium on Circuits and Systems

JF - Proceedings - IEEE International Symposium on Circuits and Systems

M1 - 4253013

T2 - 2007 IEEE International Symposium on Circuits and Systems, ISCAS 2007

Y2 - 27 May 2007 through 30 May 2007

ER -

Minimum variance spectral estimation-based time frequency analysis for nonstationary time-series

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