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Proceedings Paper

Kalman filtering and time-frequency distribution of random signals
Author(s): P. G. Madhavan; William J. Williams
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Paper Abstract

A new method to estimate the energy distribution over the time-frequency plane of time-varying stochastic signals is presented. A state space modeling approach is used to represent the signal. A Kalman-smoothing algorithm is used to estimate the states from which the so-called `Kalman- smoothed time frequency distribution (KS-TFD)' is obtained. The KS-TFD estimate is positive, has good cross-term properties and high temporal resolution. The Kalman smoother-based estimates are optimal in the mean square sense and therefore the KS-TFD estimate has excellent noise performance. We demonstrate the `localizing' property of KS- TFD using deterministic signals such as impulses and Gabor logons. Minimum interference is seen with multi component signals. For Gabor logons buried in white noise at various signal-to-noise ratios, we show the excellent performance of the KS-TFD estimate in comparison to the non-causal spectrogram using quantitative performance indices.

Paper Details

Date Published: 22 October 1996
PDF: 10 pages
Proc. SPIE 2846, Advanced Signal Processing Algorithms, Architectures, and Implementations VI, (22 October 1996); doi: 10.1117/12.255430
Show Author Affiliations
P. G. Madhavan, Univ. of Michigan (United States)
William J. Williams, Univ. of Michigan (United States)

Published in SPIE Proceedings Vol. 2846:
Advanced Signal Processing Algorithms, Architectures, and Implementations VI
Franklin T. Luk, Editor(s)

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