| Chaos-based spread spectrum code possesses many characteristics, such as big capability, high security, and so on, which can be widely applied in the DS-CDMA system. Under conditions of multipath fading and non-Gaussian ambient noise channels, the approach based on the extended Kalman filter for joint symbol detection, code delay and channel coefficient estimation has proved to have bad performance and complexity. Particle filter is very suitable for non-linear and non-Gaussian interference circumstances. Its algorithm is simple and can solve the non-linear and complex problems in multi-parameter estimation. Therefore, researching particle filtering techniques and its applications in the chaotic spread spectrum DS-CDMA system has great theoretical and practical significance.In the thesis, we first introduce the theory of particle filter techniques in detail. Both the theoretic analysis and simulation results show that particle filter does well under non-linear and non-Gaussian interference circumstances. Subsequently, we propose the application of particle filtering techniques to a challenging problem of joint symbol detection, code delay and channel coefficient estimation for the chaotic spread spectrum DS-CDMA system in multipath fading. The system can be formulated as dynamic state space models. Since the models involve both discrete and continuous-valued unknowns, we propose a particle filtering receiver based on a hybrid important sample function. Then the hybrid particle filter is integrated with Kalman filter to solve the complex and non-linear problems in multi-parameter estimation. Simulation results show that the hybrid particle filter provides better performance than the classical methods. At the end of the thesis, the applications of particle filtering techniques are extended to multi-user detection in the CDMA system and space diversity receiver, and good simulation results show its effectivity. |
PDF Full Text Request