中文摘要：

针对单载波多输入多输出（Multiple Input Multiple Output,MIMO）系统中的稀疏信道估计问题,基于压缩传感（Compressed Sensing,CS）理论,提出了一种改进的压缩采样匹配追踪（Modified Compressive Sampling Matching Pursuit,MCoSaMP）算法。新算法在现有的压缩采样匹配追踪算法的基础上,通过前一步迭代的残差设计了一种自适应加权因子,利用该加权因子进行加权最小二乘估计,逐步减小了异常样本对当前估计的影响。仿真结果表明,在使用相同长度的训练序列时,新算法与现有的基于压缩采样匹配追踪的估计算法相比,在估计精度上有明显提高。

英文摘要：

Aiming at the sparse channel estimation problem of single carrier multiple-input multiple-output（MIMO） systems,a modified compressive sampling matching pursuit（MCoSaMP） algorithm is proposed by using the compressed sensing theory.Based on the existing compressive sampling matching pursuit（CoSaMP） algorithm,the new algorithm designs an adaptive weighted factor from the previous step,and then uses the weighted least squares method to reduce the effect of the abnormal samples progressively.Simulation results show that under the same length of the training sequences,the channel estimation precision can be significantly improved by the new algorithm compared with the CoSaMP algorithm.