基于卡尔曼滤波的超宽带时变信道估计

Time-variant Channel Estimation Research Based on Kalman Filtering for UWB Systems

在超宽带时变信道估计中,针对状态转移系数估计不准确引起的滤波发散问题,提出一种基于状态转移系数门限修正的卡尔曼滤波信道估计方法。该方法对时变信道采用自回归模型(AR)进行建模,利用导频估计初始信道信息和信道状态转移系数,并对信道转移系数进行门限修正。仿真实验表明:和传统卡尔曼滤波算法相比,提出方法实现简单并能有效抑制滤波发散问题,提高时变信道估计精度。

In time-variant channel estimation for UWB system,for the filtering divergence problem caused by the inaccuracy of the state transfer coefficient,a Kalman filtering channel estimation method based on state transfer coefficient using threshold correction is proposed.The time-varying channel is modeled as an autoregressive(AR)process,then the initial channel information and channel state transfer coefficient are estimated by pilot,and the threshold of the channel state transfer coefficient is corrected.The simulation experiments show that compared with the traditional Kalman filtering algorithm,the proposed method is simple and can restrain filter divergence effectively and improve the accuracy of time-varying channel estimation.