摘要
针对国内压缩传感理论(CS)尚处于起步以及理论研究阶段,为深入阐述该理论及对其实践应用性进行探索,将压缩传感理论从信号的稀疏表示、编码测量以及重构算法3个方面展开了较为详细的论述,并深入地阐述了重构算法中具有代表性的匹配追踪、正交匹配追踪、正则化正交匹配追踪算法。并进一步介绍了最小均方差线性估计(MMSE)算法,通过与常用重构算法的仿真对比,突出了MMSE算法在低采样率下的优越性。研究结果表明,该算法在实践中具有较好的应用潜力。
Aiming at introducing and using compressive sensing（CS）into practice, CS was discussed in detail from sparse representation, encoding measurement and reconstruction algorithms. The commonly used reconstruction algorithms, such as matching pursuit, orthogonal matching pursuit and the regularized orthogonal matching pursuit, were reviewed. The minimum mean square error （MMSE） linear estimatealgorithm, which showed better reconstruction quality under low sampling rate, was introduced as well. Through the image simulation, compared with commonly used reconstruction algorithms, the results indicate that MMSE shows great superiority and potential for real applications.
出处
《机电工程》
CAS
2014年第6期805-808,818,共5页
Journal of Mechanical & Electrical Engineering
基金
国家自然科学基金资助项目(51005077)
教育部高学校博士学科点科研基金资助项目(博导类
20133514110008)
国家卫生和计划生育委员会科研基金资助项目(WKJ-FJ-27)
福建省杰出青年基金资助项目(2011J06020)
福建省质量技术监督局科技资助项目(FJQI2013095
FJQI2012024)
福建省高等学校学科带头人培养计划资助项目(闽教人[2013]71号)
关键词
压缩传感
稀疏性
信号处理
信号重构
重构算法
MMSE
compressive sensing
sparsity
signal processing
signal reconstruction algorithms
minimum mean square error（MMSE）
