The scarcity of wireless spectrum resources and high mobility have brought serious challenges to orthogonal frequency division multiplexing (OFDM) communication systems. To improve spectral efficiency and combat fast time-varying channel fading, this paper studies the OFDM system with high mobility based on superimposed pilots, and adopts a basis expansion model (BEM) to describe fast time-varying channels. An iterative channel estimation algorithm combining expectation–maximization (EM) and extended Kalman filtering (EKF) is proposed, which is called EM-EKF in this paper. It uses EM to calculate the parameter matrix, measured values and the covariance of the process noise of the unknown first-order time-varying autoregressive (TVAR) model to obtain the optimal estimation. To further enhance the system’s robustness for fast time-varying channel fading, EKF and smoothing algorithms are jointly used for time-varying channel tracking. The results show that compared with the existing representative channel estimation algorithms, the iterative EM-EKF algorithm proposed in this paper not only solves the problem of unknown prior information, but also significantly outperforms other non-iterative algorithms, and the performance of the proposed algorithm is similar to that of the iterative EKF algorithm, which is more suitable for the actual OFDM communication systems with high mobility.

无线频谱资源的稀缺性和高移动性给正交频分复用（OFDM）通信系统带来了严峻的挑战。为了提高频谱效率和对抗快速时变信道衰落，本文研究了基于叠加导频的高移动性OFDM系统，并采用基扩展模型(BEM)来描述快速时变信道。提出了一种结合期望最大化(EM)和扩展卡尔曼滤波(EKF)的迭代信道估计算法，本文称为EM-EKF。它使用EM计算未知一阶时变自回归（TVAR）模型的参数矩阵、测量值和过程噪声的协方差，以获得最优估计。为了进一步增强系统对快速时变信道衰落的鲁棒性，EKF 和平滑算法联合用于时变信道跟踪。结果表明，与现有的代表性信道估计算法相比，本文提出的迭代EM-EKF算法不仅解决了未知数的问题先验信息，但也明显优于其他非迭代算法，并且该算法的性能与迭代EKF算法相似，更适用于具有高移动性的实际OFDM通信系统。