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9 July 1992 Noise reduction methods for chaotic signals using empirical equations of motion
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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.
© (1992) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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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