TR#549: Bayesian Spectrum Estimation of Unevenly Sampled Nonstationary Data
Yuan Qi, Thomas P. Minka, and Rosalind W. Picard
To appear in: International Conference on Acoustics, Speech,
and Signal Processing, May, 2002
ABSTRACT
Spectral estimation methods typically assume stationarity and uniform
spacing between samples of data. The non-stationarity of real data is
usually accommodated by windowing methods, while the lack of
uniformly-spaced samples is typically addressed by methods that ``fill
in'' the data in some way. This paper presents a new approach to both
of these problems: we use a non-stationary Kalman filter within a
Bayesian framework to jointly estimate all spectral coefficients
instantaneously. The new method works regardless of how the signal
samples are spaced. We illustrate the method on several data sets,
showing that it provides more accurate estimation than the
Lomb-Scargle method and severalclassical spectral estimation methods.
PDF
Full
list of tech reports