Filtering Noisy Chaotic Signals And Blind Channel Equalization For Chaos-Based Communication Systems

Chaotic signals are non-periodic and bounded signals resulting from deterministic nonlinear systems. Chaotic signals are sensitive to initial conditions and look like "noise" in time domain. Because of the noise-like feature, chaotic signals occupy a wide bandwidth in frequency domain. Those characteristics mentioned above make it have potential application prospect in engineering practice. Since the self-synchronization phenomenon of chaotic system was found, the application of chaos in communications has evoked great research passion in domestic and international academe, and many kinds of "conceptive" communication systems based on chaos have been proposed.Most researches on chaos-based communication systems are based on the assumption of a ramer ideal communication environment, in which signals are transmitted without distortion or with only a moderate amount of additive noise. Real physics channel except random noises, limited bandwidth impairs the amplitude and phase of the transmitted broadband chaotic signals, the multipath effect in wireless communication channel and the time-varying feature of channel can also distort the transmitted chaotic signals. These factors affect the realization of the proposed chaos-based communication system. As done in conventional wireless communication systems, channel equalization is an important technique for combating these unwanted channel interference in chaos-based communication systems.This thesis will focus on chaos-based communication systems, and address two basic issues. One is the filtering for noisy contaminated chaotic signals, and another is the blind channel equalization for chaos-based communication systems. The start point of this thesis is to make use of adaptive filter algorithms and the modeling technique for signals.The unscented Kalman filter algorithm will be used to filter noisy contaminated chaotic signals. After expressing the filtering problem into the estimation issue of the mixed state-space model, we will study three filtering environments, i.e., except the additive Gaussian noise, the parameters of chaotic system are fixed, time-varying and step-varying, respectively. When the systematic parameters are constant, we study three chaotic systems, i.e., one-dimensional Logistic map, two-dimensional Henon map and three-dimensional Chua's circuit. It indicates by computer simulation that this filtering method can effectively reduce noise. When the systematic parameters are time-varying, we first model the time-varying parameters with the second-order autoregressive model, and then reduce the filtering problem into the estimation issue of the extended state-space model, in which the state vector consists of the time-varying parameters, the parameters of the autoregressive model and the original state variables. Finally, we realize the demodulation of a chaotic modulation communication system, in which the systematic parameters of the chaotic system parameter stands for image signal (message signal). The results indicate by computer simulation that the message signal can be recovered from certain noisy channel.The unscented Kalman filter algorithm will be applied to the blind channel equalization issue for canceling multipath fading effect in chaos-based communication systems. The simulation results for channels with constant and time-varying coefficients demonstrate that the algorithm can realize the equalization tasks.The particle filter algorithm is a sequential Monte Carlo algorithm combined with importance sampling, which can be applied to the field of state-space models and can well estimate the statistical characteristics of the stochastic variables through a nonlinear transformation. By combining with the modeling technique for signals, a blind channel equalization method for cancelling multipath fading effect in chaos-based...