Noise reduction methods for chaotic signals using empirical equations of motion

James B. Kadtke; Jeffrey S. Brush

doi:10.1117/12.138241

9 July 1992 Noise reduction methods for chaotic signals using empirical equations of motion

James B. Kadtke, Jeffrey S. Brush

Proceedings Volume 1699, Signal Processing, Sensor Fusion, and Target Recognition; (1992) https://doi.org/10.1117/12.138241
Event: Aerospace Sensing, 1992, Orlando, FL, United States

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.

Citation Download Citation

James B. Kadtke and Jeffrey S. Brush "Noise reduction methods for chaotic signals using empirical equations of motion", Proc. SPIE 1699, Signal Processing, Sensor Fusion, and Target Recognition, (9 July 1992); https://doi.org/10.1117/12.138241

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