A new Kalman filter-based power spectral density estimation for nonstationary pressure signals

Z. G. Zhang; W. Y. Lau; S. C. Chan

A new Kalman filter-based power spectral density estimation for nonstationary pressure signals

Z. G. Zhang^*
, W. Y. Lau
, S. C. Chan

^*Corresponding author for this work

The University of Hong Kong

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

This paper presents a new Kalman filter-based power spectral density estimation (PSD) algorithm for nonstationary pressure signals. The pressure signal is assumed to be an autoregressive (AR) process, and a stochastically perturbed difference equation constraint model is used to describe the dynamics of the AR coefficients. The proposed Kalman filter frame uses variable number of measurements to estimate the time-varying AR coefficients and yield the PSD estimation with better time-frequency resolution. Simulation results show that the proposed algorithm achieves a better time-frequency resolution than conventional algorithms for nonstationary pressure signals.

Original language	English
Title of host publication	ISCAS 2006
Subtitle of host publication	2006 IEEE International Symposium on Circuits and Systems, Proceedings
Pages	1619-1622
Number of pages	4
State	Published - 2006
Externally published	Yes
Event	ISCAS 2006: 2006 IEEE International Symposium on Circuits and Systems - Kos, Greece Duration: 21 May 2006 → 24 May 2006

Publication series

Name	Proceedings - IEEE International Symposium on Circuits and Systems
ISSN (Print)	0271-4310

Conference

Conference	ISCAS 2006: 2006 IEEE International Symposium on Circuits and Systems
Country/Territory	Greece
City	Kos
Period	21/05/06 → 24/05/06

Cite this

@inproceedings{5cb2f84e88fc4de6ac518a93d2847474,

title = "A new Kalman filter-based power spectral density estimation for nonstationary pressure signals",

abstract = "This paper presents a new Kalman filter-based power spectral density estimation (PSD) algorithm for nonstationary pressure signals. The pressure signal is assumed to be an autoregressive (AR) process, and a stochastically perturbed difference equation constraint model is used to describe the dynamics of the AR coefficients. The proposed Kalman filter frame uses variable number of measurements to estimate the time-varying AR coefficients and yield the PSD estimation with better time-frequency resolution. Simulation results show that the proposed algorithm achieves a better time-frequency resolution than conventional algorithms for nonstationary pressure signals.",

author = "Zhang, \{Z. G.\} and Lau, \{W. Y.\} and Chan, \{S. C.\}",

year = "2006",

language = "英语",

isbn = "0780393902",

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

pages = "1619--1622",

booktitle = "ISCAS 2006",

note = "ISCAS 2006: 2006 IEEE International Symposium on Circuits and Systems ; Conference date: 21-05-2006 Through 24-05-2006",

}

Zhang, ZG, Lau, WY & Chan, SC 2006, A new Kalman filter-based power spectral density estimation for nonstationary pressure signals. in ISCAS 2006: 2006 IEEE International Symposium on Circuits and Systems, Proceedings., 1692911, Proceedings - IEEE International Symposium on Circuits and Systems, pp. 1619-1622, ISCAS 2006: 2006 IEEE International Symposium on Circuits and Systems, Kos, Greece, 21/05/06.

A new Kalman filter-based power spectral density estimation for nonstationary pressure signals. / Zhang, Z. G.; Lau, W. Y.; Chan, S. C.
ISCAS 2006: 2006 IEEE International Symposium on Circuits and Systems, Proceedings. 2006. p. 1619-1622 1692911 (Proceedings - IEEE International Symposium on Circuits and Systems).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A new Kalman filter-based power spectral density estimation for nonstationary pressure signals

AU - Zhang, Z. G.

AU - Lau, W. Y.

AU - Chan, S. C.

PY - 2006

Y1 - 2006

N2 - This paper presents a new Kalman filter-based power spectral density estimation (PSD) algorithm for nonstationary pressure signals. The pressure signal is assumed to be an autoregressive (AR) process, and a stochastically perturbed difference equation constraint model is used to describe the dynamics of the AR coefficients. The proposed Kalman filter frame uses variable number of measurements to estimate the time-varying AR coefficients and yield the PSD estimation with better time-frequency resolution. Simulation results show that the proposed algorithm achieves a better time-frequency resolution than conventional algorithms for nonstationary pressure signals.

AB - This paper presents a new Kalman filter-based power spectral density estimation (PSD) algorithm for nonstationary pressure signals. The pressure signal is assumed to be an autoregressive (AR) process, and a stochastically perturbed difference equation constraint model is used to describe the dynamics of the AR coefficients. The proposed Kalman filter frame uses variable number of measurements to estimate the time-varying AR coefficients and yield the PSD estimation with better time-frequency resolution. Simulation results show that the proposed algorithm achieves a better time-frequency resolution than conventional algorithms for nonstationary pressure signals.

UR - https://www.scopus.com/pages/publications/34547350758

M3 - 会议稿件

AN - SCOPUS:34547350758

SN - 0780393902

SN - 9780780393905

T3 - Proceedings - IEEE International Symposium on Circuits and Systems

SP - 1619

EP - 1622

BT - ISCAS 2006

T2 - ISCAS 2006: 2006 IEEE International Symposium on Circuits and Systems

Y2 - 21 May 2006 through 24 May 2006

ER -

A new Kalman filter-based power spectral density estimation for nonstationary pressure signals

Abstract

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