摘要
压缩感知(Compressed Sensing,CS)是一种基于稀疏信号的获取和恢复的新理论,能以较小的采样代价获得完整的信号.这一理论符合无线传感网络在带宽和采集能力局限下需要低代价采样的需求.但由于无线传感网络的开放性,其容易受到环境噪声的影响,特别是采用压缩感知方法进行欠采样,虽然可以减小获取数据的开销,但这种"不完整"的欠采样数据对噪声更加敏感.因此抗噪声的健壮的重构算法能有效保证信号重构的精度.文中提出了一种近似梯度下降算法(Proximal Gradient Algorithm,PRG)对噪声下的压缩采样信号进行恢复.该算法通过逐步迭代逼近的方式,求得约束方程最优解,进而还原出原信号.通过与OMP、SP、BP算法比较,PRG算法在噪声环境下表现出较好的重构性能.
Compressed sensing（CS）is new theory for sampling and recovering signal based sparse transformation.This theory could help us to acquire complete signal at low cost.Therefore,it also satisfies the requirement of low cost sampling since bandwidth and capability of sampling is not sufficient.However wireless sensor network is an open scene,signal is easily affected by noise in the open environment.Specially,CS theory indicates a method of sub-Nyquist sampling which is effective to reduce cost in the process of data acquirement.However the sampling is＂imperfect＂and the corresponding data is more sensitive to noise.Consequently,it is urgently requisite for robust and antinoise reconstruction algorithms which can ensure the accuracy of signal reconstruction.In the article,we present a proximal gradient algorithm（PRG）to reconstruct Sub-Nyquist sampling signal in the noise environment.This algorithm iteratively uses a straightforward shrinkage step to find the optimum solution of constrained formula.Furthermore it is feasibility to recovering original signal.Finally,in the experiment,PRG shows excellent performance comparing to OMP,BP,and SP while signal is corrupted by noises.
出处
《计算机学报》
EI
CSCD
北大核心
2015年第3期614-624,共11页
Chinese Journal of Computers
基金
国家自然科学基金(61303227
61303038)
国家"八六三"高技术研究发展计划项目基金(2012AA12A401)资助~~
关键词
压缩感知
稀疏重构
无线传感网络
欠采样
物联网
compressed sensing
sparse reconstruction
wireless sensor network
sub-nyquist sampling
Internet of Things
