3 September 1993 Modeling of deterministic chaotic noise to improve target recognition
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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.
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Alastair D. McAulay, Alastair D. McAulay, Kamil Saruhan, 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); doi: 10.1117/12.154975; https://doi.org/10.1117/12.154975
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