一种稀疏度自适应超宽带信道估计算法

针对在超宽带信道估计中应用压缩感知理论需要预知信道稀疏度的问题，利用超宽带信道在时域上的稀疏性，将信道估计问题转化为压缩感知理论中的稀疏向量重构问题，提出了稀疏度自适应正则化压缩采样匹配追踪（SARCoSaMP）算法。该算法在压缩采样匹配追踪（CoSaMP）算法的基础上，引入自适应和正则化方法，自动调整所选原子数目，逐步逼近信道稀疏度K，在稀疏度未知的情况下精确地实现信道估计。仿真结果表明，该算法可有效应用于超宽带系统的信道估计，并且其性能明显优于CoSaMP算法和稀疏自适应匹配追踪（SAMP）算法。

Sparsity Adaptive Algorithm for Ultra-Wideband Channel Estimation

Ultra-wideband (UWB) channel estimation based on the theory of compressive sensing needs to predict sparsity of the channel. Considering the sparseness of the UWB channel in time domain, the problem of channel estimation can be transformed into the reconstruction of the sparse vector in compressive sensing theory. Sparsity adaptive regularization compressive sampling matching pursuit (SARCoSaMP) algorithm is proposed in this paper. The ideas of adaptive and regularization are introduced based on compressive sampling matching pursuit (CoSaMP) algorithm. The number of the selected atoms is controlled automatically in order to approach channel sparsity K gradually. The UWB channel is estimated accurately although the sparsity of the channel is not available. Results show that the proposed algorithm can be effectively used in ultra-wideband channel estimation and it is significantly superior to CoSaMP and sparsity adaptive matching pursuit (SAMP) algorithm.