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Proceedings Paper

Noise reduction methods for chaotic signals using empirical equations of motion
Author(s): James B. Kadtke; Jeffrey S. Brush
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Paper Abstract

We describe several noise reduction algorithms for signals which contain nonlinear (chaotic) components. The most promising method utilizes empirical global equations of motion as an underlying predictive model. Numerical results of the algorithm are presented, demonstrating significant improvements in SNR (up to 30 dB in a single pass) even when the input SNR is very low (0 dB or lower). Ramifications of the technique and comparisons with other methods for chaotic signal processing are discussed.

Paper Details

Date Published: 9 July 1992
PDF: 12 pages
Proc. SPIE 1699, Signal Processing, Sensor Fusion, and Target Recognition, (9 July 1992); doi: 10.1117/12.138241
Show Author Affiliations
James B. Kadtke, RTA Corp. (United States)
Jeffrey S. Brush, RTA Corp. (United States)

Published in SPIE Proceedings Vol. 1699:
Signal Processing, Sensor Fusion, and Target Recognition
Vibeke Libby; Ivan Kadar, Editor(s)

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