一种改进的自适应压缩采样匹配追踪算法  

An Improved Adaptive Compressive Sampling Matching Pursuit Algorithm

作　　者：姚成勇 林云[1] 

机构地区：[1]重庆邮电大学移动通信技术重庆市重点实验室,重庆400065

出　　处：《现代电信科技》2015年第1期18-22,29,共6页Modern Science & Technology of Telecommunications

摘　　要：针对稀疏度未知的信号,提出一种改进的自适应压缩采样匹配追踪算法(IACoSaMP)。首先在压缩采样匹配追踪算法(CoSaMP)的基本框架下,以原子相关系数最大值的一半作为一个阈值,对每次迭代中预选阶段选入的原子进行二次筛选;然后结合稀疏性自适应匹配追踪算法(SAMP),分阶段逼近实际支撑集。实验结果表明,在相同条件下,相比较于CoSaMP和SAMP,本文算法具有较优的重构性能。In this paper, an Improved Adaptive Compressive Sampling Matching Pursuit（IACoSaMP） algorithm is proposed for signal reconstruction without prior information of the sparsity. First, we adopt a new atom selection mechanism under the frame of Compressive Sampling Matching Pursuit（CoSaMP） algorithm, which takes half of the maximum value of the correlation coefficient as a threshold value to refine the atoms from the pre-selection stage in each iteration.Then we incorporate the sparsity adaptive strategy attached to the Sparsity Adaptive Matching Pursuit（SAMP） algorithm to expand the true support set stage by stage. Experimental results show that under the same conditions, the proposed algorithm performs better than Co Sa MP and SAMP.

关 键 词：压缩感知 匹配追踪 稀疏度自适应 重构算法 

分 类 号：TN911.7[电子电信—通信与信息系统]