针对高速移动场景下信道快衰落、非平稳等特性导致下行链路信道估计性能受限的问题,提出了一种适用于高速移动环境下行链路的信道估计方法.采用自回归过程对信道建模,构造自反馈的扩展卡尔曼滤波器(Extended Kalman Filter,EKF)追踪信道响应及其时域相关系数.为了解决EKF自反馈结构引起的误差传播问题,采用了迭代检测译码的接收机结构,以利用信道编码的冗余提升EKF的信道估计精度.仿真分析表明,在高速移动环境下所提方法相较于最小二乘估计和线性最小均方误差估计等传统方法提升了信道估计的均方误差和系统的误码率性能,可应用于高速列车无线通信设备的接收机基带信号处理系统.%In high-speed environment, fast fading and non-stationary limits the channel estimation performance, so we proposed a channel estimation method for high-speed mobility in downlink. The time-varying channel was modeled as an autoregressive (AR) process. So that a self-feedback extended Kalman filter (EKF) was set up to track the channel response and AR parameter. In order to eliminate the influence of error propagation in self-feedback EKF, an iterative detector & decoder receiver was adopted, by utilizing the redundancy of encoding, to improve the estimation accuracy. The simulation results show that compared with least squares (LS) and linear minimum mean square errors (LMMSE) in high speed environment, the proposed method improves the channel estimation accuracy and performance of whole system. And it could be applied to baseband signal processing of wireless receiver in high-speed train.
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