Nonlinear Kalman Filter-Based Robust Channel Estimation for High Mobility OFDM Systems

High-speed train (HST) and vehicle-to-vehicle (V2V), as typical scenes of 5G communication, have attracted extensive attention from academia and industry in recent years. Aiming at the channel characteristics of frequency-selective fading, fast time-varying and time-domain non-stationary in high mobility scenarios, a nonlinear Kalman filter-based high-speed channel estimation algorithm for orthogonal frequency-division multiplexing (OFDM) systems is proposed. We adopt basis expansion model (BEM) to eliminate the inter-subcarrier interference (ICI) caused by the fast time-varying characteristics. For the non-stationary characteristics of high mobility channel, a channel interpolation algorithm based on extended Kalman filter (EKF) is introduced to jointly estimate the channel impulse response (CIR) and time correlation coefficients. However, the EKF channel estimation uses structure of decision feedback to construct the observation matrix, which would lead to error propagation. This paper analyzes the generation of error propagation through theoretical derivation. Furthermore, for the error propagation of EKF, we introduce unscented Kalman filter (UKF) algorithm to perform Gaussian approximation of non-Gaussian observing system, and eliminate the influence by Kalman filter (KF). Simulation results demonstrate that the channel estimation accuracy of BEM-UKF is further improved compared with BEM-EKF, and the influence of pilot distance (PD) is smaller, which further improves the robustness of the algorithm.