摘要
基于贝叶斯预测技术,提出了一种实用的时变无线信道模型。在此基础上利用粒子滤波技术强大的随机搜索能力,给出了一种稳健的无线信道跟踪方案。与目前已有的方案相比,该方案不需要知道准确的信道统计特性并且能有效地降低静态低阶AR过程的建模误差。仿真结果表明该方案具有优越的信道跟踪性能和鲁棒性。
Based on the Bayesian forecasting technology, a practical time-varying wireless channel model was proposed. Further more, on the basis of the proposed channel model, utilizing the powerful stochastic search ability of particle filtering, a robust wireless channel tracking scheme was developed. Comparing with the traditional tracking scheme, this scheme doesn＇t exactly need to know the channel statistics and can greatly decrease the influence of modeling error. Simulation results also show that the proposed scheme has superior tracking performance and robustness.
出处
《系统仿真学报》
EI
CAS
CSCD
北大核心
2008年第6期1623-1625,1629,共4页
Journal of System Simulation
关键词
粒子滤波
贝叶斯预测
信道模型
信道跟踪
建模误差
particle filtering
Bayesian forecasting
channel modeling
channel tracking
modeling error
