Share Email Print

Proceedings Paper

Modeling of deterministic chaotic noise to improve target recognition
Author(s): Alastair D. McAulay; Kamil Saruhan
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

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)

© SPIE. Terms of Use
Back to Top