Optimal detection of a class of chaotic signals

Optimal estimation and detection algorithms for use with a potentially important class of discrete-time chaotic signals generated via tent maps are described. The authors develop and evaluate, in particular, maximum likelihood (ML) estimation algorithms for filtering, predicting, and smoothing these signals from noise-corrupted measurements, and they present highly efficient, recursive implementations for these nonlinear algorithms. They also develop ML detection algorithms for discriminating among classes of chaotic signals generated from tent maps, and the results are used to explore the viability of a simple paradigm for secure communication based on these chaotic signals.<>