摘要
为提高压缩感知子空间追踪算法的信号重建概率及精度,提出一种递减候选集正则化子空间追踪算法.该算法基于Co Sa MP/SP算法并加以改进,将迭代过程分成若干个阶段,在每个阶段均采用类Co Sa MP/SP算法进行迭代计算,但各阶段的候选集原子个数依次递减,同时按正则化方法选择新的候选集原子.实验仿真对比结果表明,与同类算法相比,所提出算法能够以更高概率重建信号,在噪声环境下也具有较高的重建精度.
A decreasing candidate set regularized subspace pursuit algorithm is proposed to improve the signal reconstruction probability and precision of the subspace pursuit algorithm in compressed sensing. The proposed algorithm is improved based on the Co Sa MP/SP algorithm, which divides the iterative process into several stages.Similar Co Sa MP/SP algorithm is adopted to iterative calculation during each of the stages. The number of candidate set atoms is successively decreasing in each stage, and the new candidate set atoms are selected by using the regularization method. Compared to other algorithms, the simulation results show that the proposed algorithm can reconstruct the signal with higher probability and has high reconstruction precision in the noise environment.
作者
田金鹏
刘小娟
郑国莘
TIAN Jin-peng LIU Xiao-juan ZHENG Guo-xin(School of Communication and Information Engineering Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai University, Shanghai 200072, China)
出处
《控制与决策》
EI
CSCD
北大核心
2017年第2期287-292,共6页
Control and Decision
基金
国家自然科学基金项目(61132003
61571282)
上海大学创新基金项目(sdcx2012041)
关键词
压缩感知
信号重建
子空间追踪
稀疏表示
正则化
compressed sensing
signal reconstruction
subspace pursuit
sparse representation
regularization
