Modeling of deterministic chaotic noise to improve target recognition
Alastair D. McAulay, K. Saruhan · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1993
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.