摘要
针对压缩感知中未知稀疏度信号的重构问题,提出了一种改进的正则化自适应匹配追踪算法。它通过自适应变步长迭代对信号稀疏度进行估计,并将其作为初始支撑集长度,然后在分阶段迭代中正则化筛选原子,最终实现信号的精确重构。仿真结果表明,该算法重构信号的性能和效率均优于子空间追踪算法、正交匹配追踪算法和稀疏度自适应匹配追踪算法。
This paper presents a modified regularization adaptive matching pursuit（ MRAMP） algorithm for the problem that reconstruct signals with unknown sparsity in compressed sensing. The proposed algorithm adaptively estimates the sparsity with difference steps through stage by stage and set it to the length of the initial support,then gets the accurate target signal by regularization screening of atoms in every stage.Simulation results show that the performance and efficiency of the proposed algorithm is better than subspace pursuit（ SP） algorithm,orthogonal matching pursuit（ OMP） algorithm and SAMP algorithm.
出处
《杭州电子科技大学学报（自然科学版）》
2015年第1期79-83,共5页
Journal of Hangzhou Dianzi University：Natural Sciences
关键词
信号重构
压缩感知
稀疏度
自适应
正则化
signal reconstruction
compressive sensing
sparsity
adaptation
regularization
