基于历史信息的软卡尔曼滤波迭代时变信道估计方法

针对高速移动MIMO-OFDM系统，提出了一种基于历史信息的软卡尔曼滤波迭代时变信道估计方法。考虑高速铁路环境中不同列车在相同位置处的信道具有很强的相关性，首先利用历史列车的信道信息获取最优基函数，基于该基函数对信道建模，将对信道的估计转换成基系数的估计，降低了计算复杂度和提高了信道估计精度。其次，在每次迭代中采用了软卡尔曼滤波和数据检测联合的方法估计基系数；为了更好地减少数据检测误差传播的影响，采用软数据检测方法，并且在每次迭代中将软数据检测误差作为噪声进行处理。另外，采用的软卡尔曼滤波器不涉及 AR 模型跟踪因子，避免了估计跟踪因子引入的计算复杂度。仿真结果表明，所提方法具有更好的估计性能，且更适用于实际的高速移动场景的时变信道获取。

Historical information based iterative soft Kalman time-varying channel estimation method

For high-speed mobile MIMO-OFDM systems,a historical information based iterative soft-Kalman filter time-varying channel estimation method was proposed.Considering that the channels experienced by different trains in the high-speed railway environment have strong correlation,the channel information of the historical train was firstly used to obtain the optimal basis function,which can be employed to model the channel.By the optimal basis function,the computational complexity was reduced and the channel estimation accuracy was improved for the proposed method.Secondly,the soft-Kalman filter and data detection were jointed to estimate the base coefficient in each iteration.To reduce the effect of data detection error propagation on the channel estimation,the soft data detection scheme was employed and the soft detection error was treated as noise in each iteration.In addition,the soft-Kalman filter used in the proposed method does not involve the AR model tracking factor,thereby avoiding the computational complexity introduced by the estimated tracking factor.The simulation results show that the proposed method has better estimation performance,and is more suitable for time-varying channel acquisition of actual high-speed mobile scenarios.