Publication:
The use of window functions and Kalman filtering in spectral estimation

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Authors
Go, William W.
Subjects
Signal processing
Kalman filter
periodogram
spectral analysis
Advisors
Hippenstiel, Ralph
Date of Issue
1990-03
Date
March 1990
Publisher
Monterey, California. Naval Postgraduate School
Language
en_US
Abstract
The periodogram, the square of the magnitude of the Fourier Transform, is widely used to estimate the spectral content of sampled processes. The performance of the periodogram is degraded by spectral leakage. This is the consequence of processing finite-length data records. Classical means of enhancing periodogram performance are the use of tapered window functions and averaging of several periodograms. These methods smooth the spectral estimate, but at a loss of resolution. A non-stationary Kalman filter was applied to the periodogram of untapered (i.e., rectangular windowed) time data in an effort smooth the noise portions of the periodogram while leaving the main spectral response unaltered. The Kalman filter was able to enhance the periodogram. Best results were obtained in the single spectral peak case. Even in the case of multiple spectral peaks, the resolution of the unfiltered periodogram was largely preserved since the filtering algorithm was designed to selectively smooth the noise-only segments of the spectral estimate.
Type
Thesis
Description
Series/Report No
Department
Department of Electrical and Computer Engineering
Organization
Naval Postgraduate School (U.S.)
Identifiers
NPS Report Number
Sponsors
Funder
Format
v, 130 p. bill
Citation
Distribution Statement
Approved for public release; distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.
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