摘要
提出了一种基于粒子滤波的OFDM时变信道和模型参数的联合估计方法.该方法将时变信道建成一个动态时变参数AR模型,在传统粒子滤波算法的基础上,引入核平滑收缩技术动态估计模型参数,进而估计信道状态,最终实现了状态方程参数和信道状态的联合估计.仿真结果表明:与传统的采用常系数AR模型的信道估计方法相比,该方法在估计精度和系统性能方面均有明显的改善.
A joint estimation method of the model parameter and time-varying channel states,which was based on the particle filter（PF）algorithm,was proposed for OFDM system.By modeling the time-varying channel for AR model of dynamic and time-varying coefficient,the model parameter was estimated by introducing the particle filter algorithm and kernel smoothing contraction technology.Besides,channel states were estimated latter.Finally,it realizes the joint estimation of the channel states and the model parameter.Simulation results show that the above mentioned method has more significantly improved in the estimation precision and system performance compared with the traditional channel estimation method with AR model of constant coefficient.
出处
《郑州大学学报（工学版）》
CAS
北大核心
2011年第2期84-87,92,共5页
Journal of Zhengzhou University（Engineering Science）
基金
国家自然科学基金资助项目(60702020)
关键词
时变信道
动态估计
粒子滤波
核平滑收缩
time-varying channel
dynamic estimation
particle filter
kernel smoothing contraction
