电能质量信号压缩采样匹配跟踪算法研究  

Research on CoSaSAMP Algorithm for Power Quality Signal

作　　者：刘传洋[1] 孙佐[1] 刘景景[1] 方曙东[1] 李春国 宋康[3] LIU Chuanyang;SUN Zuo;LIU Jingjing;FANG Shudong;LI Chunguo;SONG Kang(Department of Mechanical and Electrical Engineering,Chizhou College,Chizhou 247000,China;School of Electronic Science and Engineering,Southeast University,Nanjing 210096,China;School of Electronic Information,Qingdao University,Qingdao 266071,China)

机构地区：[1]池州学院机电工程学院,安徽池州247000 [2]东南大学电子科学与工程学院,江苏南京210096 [3]青岛大学电子信息学院,山东青岛266071

出　　处：《安徽工程大学学报》2020年第2期52-58,共7页Journal of Anhui Polytechnic University

基　　金：国家自然科学基金资助项目(NSFC61671144);山东省自然科学基金资助项目(ZR2017BF028);安徽省优秀拔尖人才基金资助项目(GXYQ2019109、GXGNFX2019056);池州学院研究基金资助项目(2018XJPKC12、2017XWTXM07)。

摘　　要：电能质量信号具有实时随机性,针对匹配跟踪算法在稀疏度未知的情况下难以精确重构电能质量信号的缺陷问题,提出了稀疏度自适应的压缩采样匹配追踪算法。引入稀疏度估计方法提前对信号的初始稀疏度进行迭代估计,利用递归思想通过残差变化动态调整稀疏度逼近信号的真实稀疏度,通过最小二乘法重构出电能质量信号最优估值。实验结果表明,在测量矩阵为二元块对角矩阵的基础上,所提出的算法与压缩采样匹配跟踪算法与自适应匹配追踪算法相比,信噪比提高了10~20 dB,具有抗干扰能力强、重构精度高的优点。Power quality signal real-time randomness,in view of the problem of CoSaMP under the condition of the sparsity unknown,is difficult to accurately reconstruct. The paper puts forward a sparsity adaptive matching pursuit algorithm. Sparsity estimation method is brought forward on the initial signal sparsity of iterative estimation,based on the recursive thought by using residual to change dynamically adjust approximation signal real sparsity,to reconstruct the original signal optimal estimate by least squares method. Experimental results show that,on the basis of binary block diagonal measurement matrix,compared with CoSaMP algorithm and SAMP algorithm,CoSaSAMP algorithm improves SNR by 10~20 dB,having the advantages of strong anti-interference ability and high reconstruction accuracy.

关 键 词：电能质量 压缩采样 自适应 递归 稀疏度 

分 类 号：TN91[电子电信—通信与信息系统]