Modeling of deterministic chaotic noise to improve target recognition

Alastair D. McAulay; Kamil Saruhan

doi:10.1117/12.154975

3 September 1993 Modeling of deterministic chaotic noise to improve target recognition

Alastair D. McAulay, Kamil Saruhan

Proceedings Volume 1955, Signal Processing, Sensor Fusion, and Target Recognition II; (1993) https://doi.org/10.1117/12.154975
Event: Optical Engineering and Photonics in Aerospace Sensing, 1993, Orlando, FL, United States

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.

Citation Download Citation

Alastair D. McAulay and Kamil Saruhan "Modeling of deterministic chaotic noise to improve target recognition", Proc. SPIE 1955, Signal Processing, Sensor Fusion, and Target Recognition II, (3 September 1993); https://doi.org/10.1117/12.154975

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