摘要
观测矩阵是压缩感知理论的重要研究内容,然而已有的观测矩阵没有与重构效果相关联,存在不稳定和重构精度低等缺陷.为了提高信号重构的效果,提出以高斯观测矩阵为基础,以重构误差为目标函数,采用标准蝙蝠算法对观测矩阵进行优化.为了验证所提算法的效果,以信号和图像为例,与其余5个算法进行比较,仿真结果表明,所提算法具有较大的稳定性和较高的重构精度.
The measurement matrix plays an important role in compressed sensing theory. However, the measurement matrix is only dominated by the compressed signal, so it is influenced by the signal reconstruction significantly. Therefore,an optimization algorithm for the measurement matrix with the bat algorithm is introduced. In this algorithm, the measurement matrix is initialized by using the Gaussian matrix, and then updated with the bat algorithm, while the signal reconstruction error is taken as the objective function. To verity the performance, the proposed algorithm is compared with five other algorithms. Simulation results show that the proposed algorithm has better stabity and reconstruction accuracy.
作者
崔志华
张春妹
时振涛
牛云
CUI Zhi-hua;ZHANG Chun-mei;SHI Zhen-tao;NIU Yun(School of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan 030024, Chin)
出处
《控制与决策》
EI
CSCD
北大核心
2018年第7期1341-1344,共4页
Control and Decision
基金
山西省自然科学基金项目(201601D011045)
关键词
压缩感知
信号重构
观测矩阵
蝙蝠算法
重构误差
compressed sensing
signal recovery
measurement matrix
bat algorithm
reconstruction error
