Given the sparse structure of Ultra Wide-Band (UWB) channels, Compressive Sensing (CS) is exploited for UWB channel estimation. Muti-Task Compressive Sensing (MTCS), as a CS implementation, has exhibited a potential for promoting signal reconstruction. The signal parameters and data sharing can be solved using the Gamma-Gaussian prior. In this paper, the Hierarchy Dirichle processing (HDP) provides the tree structure of the HDP prior for data sharing across multiple tasks. We research the channel estimation performance of HDP Hidden Markov Model based Muti-Task Compressive Sensing (HDP-HMM-MTCS) for UWB communication systems. In particular, investigate the effects of three factors. Firstly, the sparse structure of a standardized IEEE 802.15.4a channel under Line-Of-Sight (LOS) and Non-Line-Of-Sight (NLOS) environments is estimated. Secondly, the CS Rate (CSR) regions' effect on the HDP-HMM-MTCS channel estimation performance is calculated. Thirdly, the SNR regions are compared with the results of the MTCS, Simple-Task Compressive Sensing (STCS), Orthogonal Matching Pursuit (OMP), and the L1 magic estimations. The simulation results demonstrate that the HDP-HMM-MTCS has the minimum executable time and its channel estimation performances exceed those of the MTCS and the other algorithms, regardless of the LOS and NLOS environments. Therefore, the HDP-HMM-MTCS is an effective and efficient UWB channel estimation method for a sparse channel mode.%给定超宽带(Ultra Wide-Band, UWB)信道的稀疏结构, 利用压缩感知(Compressive Sensing, CS)进行UWB信道估计. 作为CS实现的多任务CS (Muti-Task Compressive Sensing, MTCS)算法进行信号重建. 信号参数和数据共享可以使用伽马-高斯先验来求解. 在本文中, 层次结构Dirichle进程(Hierarchy Dirichle Processing, HDP)提供了HDP的树结构, 用于解决跨多个任务的数据共享问题. 我们研究UWB通信的隐马尔可夫模型(Hidden Markov Model, HMM) HDP多任务CS (Hierarchy Dirichlet Processing Hidden Markov Model based Muti-Task Compressive Sensing, HDP-HMM-MTCS)的信道估计性能. 首先, 在视距(Line-Of-Sight, LOS)和非视距(Non-Line-Of-Sight, NLOS)环境下的标准化IEEE 802.15.4a信道的稀疏信道结构估计. 其次, CS比率(CS Rate, CSR)对HDP-HMM-MTCS信道估计性能的影响. 最后, 利用SNR (Signal-to-Noise Ratio), 并将其与MTCS, STCS(Simple-Task Compressive sensing), OMP (Orthogonal Matching Pursuit), L1magic算法以及新的算法如改进的贝叶斯压缩感知(Bayesian Compressive Sensing, BCS)算法, 多经字典自适应算法BCS和特征字典自适应算法BCS的信道估计比较时间复杂性. 仿真结果表明, 无论LOS和NLOS环境如何, HDP-HMM-MTCS具有最小可执行时间, 其信道估计性能优于MTCS和其他算法. 因此, HDP-HMM-MTCS是用于稀疏信道模式的有效且高效的UWB信道估计方法.
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