Multiharmonic tracking using sigma-point Kalman filter

Author

Kim, Sunghan ; Paul, Anindya S. ; Wan, Eric A. ; McNames, James

Author_Institution

Biomedical Signal Processing Laboratory, Portland State University, Portland, Oregon, U.S.A.

fYear

2008

fDate

20-25 Aug. 2008

Firstpage

2648

Lastpage

2652

Abstract

Several groups have proposed the state-space approach to track time-varying frequencies ofmulti-harmonic quasi-periodic signals contaminated with white Gaussian noise. We compared the extended Kalman filter (EKF) and sigma-point Kalman filter (SPKF) algorithms on this problem. On average, the SPKF outperformed the EKF and more accurately tracked the instantaneous frequency over a wide range of signal-to-noise (SNR) ratios.

Keywords

Biomedical engineering; Biomedical signal processing; Frequency estimation; Hidden Markov models; Integral equations; Laboratories; Nonlinear filters; Power harmonic filters; Signal sampling; State-space methods; Extended Kalman filter; Sigma-Point Kalman filter; instantaneous frequency (IF); local minima issue; normalized frequency mean-square-error (NFMSE); Algorithms; Automatic Data Processing; Electrophysiology; Image Enhancement; Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional; Models, Biological; Models, Statistical; Models, Theoretical; Normal Distribution; Signal Processing, Computer-Assisted; Software;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE

Conference_Location

Vancouver, BC

ISSN

1557-170X

Print_ISBN

978-1-4244-1814-5

Electronic_ISBN

1557-170X

Type

conf

DOI

10.1109/IEMBS.2008.4649746

Filename

4649746