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Proceedings Paper

Modeling of deterministic chaotic noise to improve target recognition
Author(s): Alastair D. McAulay; Kamil Saruhan
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Paper Abstract

We discuss three measures to determine whether a given noise time sequence or time varying image has a deterministically generated chaotic component and the strength of that component: Lyapunov coefficients, Kolmogorov entropy, and fractal dimension. Results of computer experiments show that either a neural network or a polynomial model may be successfully used to model a logistic function chaotic sequence generator. Polynomials are also shown to model a Lorentz system. In all cases, the model generates chaotic noise with the same measures as the real noise data.

Paper Details

Date Published: 3 September 1993
PDF: 8 pages
Proc. SPIE 1955, Signal Processing, Sensor Fusion, and Target Recognition II, (3 September 1993); doi: 10.1117/12.154975
Show Author Affiliations
Alastair D. McAulay, Lehigh Univ. (United States)
Kamil Saruhan, Lehigh Univ. (United States)

Published in SPIE Proceedings Vol. 1955:
Signal Processing, Sensor Fusion, and Target Recognition II
Ivan Kadar; Vibeke Libby, Editor(s)

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