【作者】 胡波；

【导师】 赵知劲；

【作者基本信息】 杭州电子科技大学 ， 信号与信息处理， 2012， 硕士

【摘要】 通信信号处理通常将通信系统建模为线性、高斯动态空间模型，然而事实上发射功率放大器并非完全工作在线性状态，室内外人为和自然的电磁干扰使得无线通信中存在着大量的冲击噪声，因此无线通信的信号处理问题实际上是非线性、非高斯系统的状态估计问题。粒子滤波(PF)在非线性和非高斯模型下具有独到优势，对其进行研究具有重要的理论意义与应用价值。本文主要研究粒子滤波算法在多用户检测、混沌信号分离中的应用。首先，从贝叶斯估计和蒙特卡罗(Monte Carlo)方法出发，详细阐述了粒子滤波的理论基础和算法的基本原理，并给出了完整的算法流程。其次，研究了基于粒子滤波算法的时变多用户检测。传统多用户检测方法通常假定系统的活跃用户数固定不变，其一般为这个系统所能容纳的最大用户个数。在此前提下，传统多用户检测方法能够获得较好的性能。然而在实际多址移动通信系统中活跃用户个数及其参数往往都是时变的，此时传统多用户检测方法性能不佳。因此，有必要寻求一种动态估计方法，以实现活跃用户数目、用户参数以及用户数据的联合估计。针对这个问题，本文利用随机集理论建立多用户动态模型，对于活跃用户个数时变的CDMA系统，研究了基于马尔科夫蒙特卡罗粒子滤波的多用户检测方法，实现了用户状态和数据的联合估计；对于活跃用户个数及其幅度皆时变的CDMA系统，提出了采用Rao-Blackwellised粒子滤波（RBPF）算法的时变多用户检测器，实现了活跃用户数目变化和幅度变化的跟踪及用户发送数据估计。算法在误码率、系统容量、远近效应等方面的性能结果表明，本文算法性能明显优于传统的最优多用户检测算法（OMD）和基本粒子滤波(PF)算法.最后，研究了动态环境下基于粒子滤波的混沌信号盲分离。当源信号个数固定时，传统的分离方法能够获得较好的分离效果。但实际环境中源信号个数是时变的，此时传统方法难以实现盲分离。本文先利用随机集模型拟合源信号个数的时变情况，然后以粒子滤波算法跟踪活跃信号数目，同时实现混沌信号分离。仿真结果表明，本文提出的算法能够有效地实现源信号个数时变情况下的混沌信号分离。更多还原

【Abstract】 Communication systems are usually modeled as linear Gaussian dynamic ones incommunication signal processing. But actually the transmit power amplifier does not completelywork in the linear region and there exists large amount of impact noise created by the natural andartificial electromagnetic interference outside in the wireless communication environment, thus thesignal processing problem in wireless communication is actually a state estimation problem ofnon-linear and non-Gaussian systems. Since particle filtering(PF) gains significant advantages innon-linear and non-Gaussian models, the research on it is both meaningful theoretically andvaluable practically.This thesis mainly studys about the application of PF in the multiuser detection and blindseparation of chaotic signal.Firstly, starting from Bayesian estimation and Monte Carlo method, the basic principles ofparticle filtering algorithms are introduced, and the complete algorithm flow is given.Secondly, the time-varying multiuser detection based on PF algorithm is researched.Conventional multiuser detection methods usually assume that the amount of active users in thewhole system is a constant number which is commonly chosen to be equal to the maximal numberof users the system can contain. Conventional multiuser detection methods perform well under thisassumption. But the number of active users and their parameters are often time-varying in practicalmulti-access mobile communication systems, consequently the performance of the conventionalmultiuser detection methods is seriously deteriorated. Thus, it is necessary to find out a dynamicestimation method to achieve joint estimation of the number of active users, the parameters and dataof the users. In view of this problem, in this paper, the multiuser dynamic model is formed utilizingrandom set theory. When the number of active users is time varying in CDMA systems, themultiuser detection method based on Markov Mente Carlo PF is researched and the joint estimationof user’s state and data is achieved. When both the number of active users and their amplitudes aretime varying in CDMA systems, the time-varying multiuser detector based on Rao-Blackwellisedparticle filter(RBPF) algorithm is proposed not only to trace the number of active users and thechange of their amplitudes but also to estimate the users’ transmitted data. BER, system capacityand near-far effect of the proposed algorithm are given. Simulation results show that the proposedalgorithm performs better than conventional OMD algorithm and basic PF algorithm .Finally, blind separation based on PF for chaotic signal in dynamic environment is studied. Theconventional separation methods perform well when the number of source signals is constant. But in practice the number of source signals is time varying. Under this condition, conventionalmethods can hardly achieve blind separation. In this paper, random set theory is used to deal withthe case of time-varying of source signals number, PF tracks the number of active source signals,and then chaotic signals are blind separated. Simulation results show that the proposed algorithmcan achieve the effectively separation of chaotic signal under the condition that the number ofsource signals is time-varying.更多还原