物理学报Issue(7)：1-8,8.DOI:10.7498/aps.64.070201
基于块稀疏贝叶斯学习的多任务压缩感知重构算法
A recovery algorithm for multitask compressive sensing based on block sparse Bayesian learning
摘要
Abstract
As a widely applied model for compressive sensing, the multitask compressive sensing can improve the performance of the inversion by appropriately exploiting the interrelationships of the tasks. The existing multitask compressive sensing recovery algorithms only utilize the statistical characteristics of a sparse signal, the structural characteristics of the sparse signal have not been taken into consideration. A multitask compressive sensing recovery algorithm is proposed in this paper based on the block sparse Bayesian learning. The block sparse single measurement vector model is applied to the multi-task problem. Both statistical and block structural characteristics of the sparse signal are used to build a mathematical model, and the sparse inverse problem is linked to the parameter iteration problems in the Bayesian framework. The proposed algorithm does not require the sparseness information and noise beforehand, which turns out to be an effective blind recovery algorithm. Extensive numerical experiments show that the proposed algorithm can exploit both statistical and structural characteristics of the signal, therefore it may reach a good trade-off between the recovery accuracy and the convergence rate.关键词
多任务压缩感知/稀疏贝叶斯学习/块稀疏框架Key words
multitask compressive sensing/sparse Bayesian learning/block sparse framework引用本文复制引用
方青,张弓,贲德..基于块稀疏贝叶斯学习的多任务压缩感知重构算法[J].物理学报,2015,(7):1-8,8.基金项目
国家自然科学基金(批准号:61201367,61271327,61471191)、南京航空航天大学博士学位论文创新与创优基金(批准号:BCXJ14-08)、江苏省研究生培养创新工程(批准号：KYLX_0277)、中央高校基本科研业务费专项资金和江苏高校优势学科建设工程(PADA)资助的课题 （批准号:61201367,61271327,61471191）
