基于压缩感知超宽带信号盲稀疏度信道估计

Channel Estimation Based on Compressed Sensing UWB Signal Blind Sparsity

鉴于超宽带(UWB)信道估计要求预先给出信道才能精确重构的不足,研究了基于压缩感知的盲稀疏度匹配追踪类算法用于信道重建。这种盲稀疏度方法根据迭代终止条件和字典中最优原子选择方式的不同,设置迭代终止阈值和阶段转换阈值,通过可变步长的增大逐步逼近稀疏度,实现精确重建。仿真结果表明,相同条件下,基于此思想经过改进算法可有效用于解决实际UWB信道估计,较改进前算法估计性能相当,是一种具有应用价值的盲稀疏度重构方法。

In view of the shortcoming that ultra-wideband （UWB） channel estimation requires sparsity as the prior information in order to accurately reconstruct, the blind sparsity iterative greedy reconstruction algorithm based on compressed sensing for the channel estimation is studied. This blind sparsity method sets the iteration termination threshold and stage conversion threshold according to the differences between the iteration tennination condition and the way of choice of optimal atom in dictionary, and through increasing variable step, sparsity is gradually approximated to achieve accurate reconstruction. The simulation results show that, under the same conditions, based on this idea the improved algorithm can effectively be used to solve practical UWB channel estimation. The estimation performance is equivalent to that of previous algorithm, and the blind sparsity reconstruction algorithm has a certain application value.