一种具有鲁棒性的子空间盲信道估计算法

摘要

Due to the performance degradation of deterministic blind channel identification algorithms under the overestimation of channel order, a FOCUSS-based subspace method is proposed for blind channel identification, which is robust to channel order overestimation.Based on the sparsity of the desirable channel estimation to order overestimation, a weighted constraint is used to search the sparse solution, combining with the FOCUSS algorithm, and then augmented Lagrange function is applied to improve the robustness of the FOCUSS algorithm.The simulation results show proposed algorithm owns better robustness to order overestimation than existing algorithms.%针对确定性盲信道估计算法在信道阶数未知条件下性能恶化问题,该文提出了一种对于信道阶数过估计具有鲁棒性的子空间盲信道估计方法.在信道阶数过估计条件下,基于传统子空间算法的理想解具有一定稀疏特性,该文通过增加加权约束条件并利用FOCUSS算法重构恢复其稀疏解.同时,为了避免算法中病态性问题的出现,引入增广拉格朗日函数法来改善算法稳健性.理论分析和仿真表明,选择适当的加权因子可使论文算法在较大阶数过估计(过估计3阶以上)情况下保证信道估计性能稳健,算法性能具有较强的阶数过估计鲁棒性且优于现有其他算法.