Particle Filter. Chaotic Communications Technology Research

The nonlinear signal processing is the difficult and hot topic in signal processing. Only a few narrow classes of models have exact solutions, and a number of approximate filters have been devised for more generalized cases. However, these traditional filters may be easy to get in local minimum and face the huge computation cost. Combining Bayesian theory with Monte Carlo sampling, particle filtering provides a flexible way to solving nonlinear problems. Chaos signal is the typical nonlinear signal. The nonlinearity and long time unpredictable property of chaotic signal cause the filtering problems of chaos communication difficult to conduct. Chaos synchronization, channel equalization, multi-user signal detection are the key problems in chaotic communications for practical implementation. In this dissertation, based on particle filtering, chaotic signal analysis, channel estimation and chaotic communication signal detection are studied in detail. Extended to regular wireless communication, the important technique for particle filtering-the sampling dimensionality decreasing is also discussed. The main work of my research is as follows:1) We study the chaotic synchronization based on extended Kalman filter (EKF) and particle filtering. We analyze degenerate phenomena of EKF. And a robust chaotic synchronization method based on particle filtering is proposed. Utilizing the Cramer-Rao low bound, an adaptive variance choice strategy for roughing noise is developed.2) In multi-user environments, a online blind separation algorithm based on particle filtering is proposed. Further more, a novel delay estimation method is also suggested, which can effectively reduce the residual noise in the recovered signal compared to the traditional delay-weight method.3)For the flat fading channel environments, based on Bayesian forecasting technology, a time-varying wireless channel model is proposed. Utilizing particle filtering and the channel model, a robust wireless channel tracking scheme is developed. Compared with the traditional tracking scheme, this scheme doesn't need to know exactly the normalized doppler frequency, and can greatly decrease the modeling error.4) For the chaos masking communication scheme using encryption function, a novel Bayesian receiver is provided. Although there is not additional synchronization signal, the proposed technique can easily synchronize the chaotic system in transmitter utilizing the received signal which has unknown information. Further more, in order to decrease the complexity and improve the performance of the proposed receiver, a suboptimal importance function is suggested, which combines the prior distribution of chaotic state and the posterior distribution of information symbols.5) For the chaos communication scheme which masks the information signal in the chaotic frequency modulation signal, particle filtering for frequency tracking is introduced, and also its feasibility is analyzed. The posterior Cramer-Rao Bounds for the frequency tracking of the chaotic frequency modulation signal is also derived. The simulation demonstrates the superiorities of particle filtering.6) The key technique of particle filtering- the sampling dimensionality decreasing for the general environment is studied. In the background of MIMO frequency selective channel estimation, a time delay domain particle filtering is suggested. The main idea is to change the time domain channel estimation into time delay domain processing, and a bank of particle filters is utilized, thus the sampling dimensionality is much small for each particle filters.