基于Kalman滤波的MIMO时变信道估计

研究了一种基于Kalman滤波的MIMO时变信道估计与跟踪问题。利用衰落信道功率谱统计特性的先验信息，将信道冲击响应近似为一个低阶自回归滑动平均过程，通过信道传输函数逼近信道功率谱的幅频特性，建立时变衰落单径信道的状态方程，导出MIMO信道状态模型参数，并通过Kalman滤波跟踪信道的时变特性。理论分析和仿真试验表明，该算法在时变信道下具有较好的性能，和传统信道估计方法相比，接收机性能有了较大的改进。

MIMO Time-varying Channel Estimation Based on Kalman Filtering

This paper proposes a novel algorithm which uses the Kalman filtering based on Clarke’s model to track the Multiple-Input Multiple-Output(MIMO) time-varying multi-path fading channel. The channel transport function approximates the square root of the spectrum density, and a state-space model for the fading channel can be built based on the channel transport function. Combining the prior information of the time-varying fading channel power spectral density, MIMO channel state-space model can be obtained. Theoretical analysis and simulations show the algorithm is effective for the estimation of the fading channel when the performance of the channel estimation is presented in terms of the Mean-Square Error(MSE).