基于改进强跟踪无迹卡尔曼滤波的正交频分复用频偏跟踪和估计算法

Frequency offset tracking and estimation algorithm in orthogonal frequency division multiplexing based on improved strong tracking unscented Kalman filter

针对高速运动环境下多普勒效应导致的载波频偏,建立了正交频分复用(OFDM)动态状态空间模型,提出了基于改进的强跟踪无迹卡尔曼滤波(STUKF)的频偏跟踪和估计算法。该算法将强跟踪滤波思想跟UKF相结合,通过在计算量测预测协方差和互协方差时引入渐消因子,在调整前一时刻频偏估计误差协方差的同时又控制过程噪声协方差,实时调整增益矩阵,增强了对时变频偏的跟踪能力,提高了估计精度。最后分别在非时变和时变频偏模型下对所提算法进行了仿真验证。仿真结果表明,与UKF频偏估计算法相比,所提算法在时变频偏中具有更好的跟踪和估计性能,在相同误码率(BER)下信噪比(SNR)大约有1 dB的提升。

Towards the large frequency offset caused by Doppler effect in high speed moving environment, a dynamic state space model of Orthogonal Frequency Division Multiplexing （OFDM） was built, and a kind of frequency offset tracking and estimation algorithm in OFDM based on improved Strong Tracking Unscented Kalman Filter （STUKF） was proposed. By combining strong tracking filter theory and UKF together, the fading factor was introduced during the process of calculating the measurement predictive covariance and cross covariance. The frequency offset estimation error covariance was adjusted; meanwhile, the process noise covariance was also controlled, and the gain matrix was adjusted in real-time. So the tracking ability to time-varying frequency offset was enhanced and the estimated accuracy was raised. The simulation test was carried out in time-invariant and time-varying frequency offset models. The simulation results show that the proposed algorithm has better tracking and estimation performance than the UKF frequency offset estimation algorithm, the Signal-to-Noise Ratio （SNR） raises about 1dB under the same Bit Error Rate （BER）.