Abstract
On the basis of nonlinear dynamical modeling we investigate a chaos-based detector, which allows the extraction of signal frequencies in noisy chaotic interference. The detection scheme is tested by using both computer-generated chaotic data and real-life Lorenz-Stenflo (LS) chaotic circuit data respectively. The performance analysis demonstrates that signals hidden beneath the chaotic ambient noise floor can be detected. By using automatic estimation of regularisation parameters for the training data in a radial basis function (RBF) neural network, it is found that the detection performance may be improved. Physically, this implies that a chaotic synchronization-based secure communication system can be successfully attacked by use of a chaos-based detector.
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