本文提出了一种应用于Alamouti空时分组码系统的联合数据检测和信道估计方案.该方案采用了一种可跟踪信道统计特性变化的新的动态信道模型,并提出了相应的自适应卡尔曼信道估计方法.该方法利用序贯更新先验信息的序贯可信度最大化方法估计系统方程的噪声方差,不需要估计最大多谱勒频移.与传统的卡尔曼信道估计相比,该方法具有较低的复杂度.仿真结果表明,本文所提方法的性能优于传统卡尔曼信道估计,特别是对于不同的最大多谱勒频移环境具有鲁棒性,具有实际应用的价值。
In this paper, a novel joint data detection and channel estimation scheme is proposed for Alamouti space-time block coding (STBC) systems. By taking the time-varying characteristic of the channel statistics into account, a new dynamic channel model is adopted in this scheme. According to this model, a channel estimator based on adaptive Kalman filter is also proposed. By applying the sequential evidence maximization with sequentially updated prior method to estimate the noise variance of system equation, the estimation of maximum Doppler frequency shift is not required in this proposed scheme. The proposed estimator has lower complexity than that of conventional Kalman estimator. Meanwhile, simulation results show that the proposed estimator has better performance than conventional Kalman estimator. Furthermore, this proposed scheme is robust to different maximum Doppler frequency shift.