基于卡尔曼一致滤波的分布式稀疏信号重构优化算法  

An Optimized Algorithm of Consensus based Kalman Filtering with Distributed Sparse Signal Reconstruction

作　　者：张朝霞[1] 李丽霞[1] 罗智勇[1] 刘纪平[1] ZHANG Zhaoxia;LI Lixia;LUO Zhiyong;LIU Jiping(School of Automation and Information Engineer,Hunan Chemical Industry Vocation Technology College,Zhuzhou Hunan 412004)

机构地区：[1]湖南化工职业技术学院,湖南株洲412004

出　　处：《软件》2021年第8期73-75,97,共4页Software

基　　金：湖南省教育厅科学研究项目(17C0552)。

摘　　要：针对无线传感器网络稀疏信号重构问题,提出了一种基于嵌入伪测量的平方根无迹卡尔曼一致滤波(SRUKCF)的分布式稀疏非线性信号重构算法。融合来自无线传感器网络中不同节点的随机线性测量值,使各滤波器对稀疏非线性信号的估计达到一致。仿真结果验证了该算法的有效性。In order to deal with the problem of sparse signal reconstruction for wireless sensor networks(WSNs),an distributed sparse signal reconstruction Optimized algorithm based on square root unscented kalman consensus filter(SRUKCF)with embedded pseudo-measurement is proposed in this paper.By fusing the random linear measurements from different nodes in the WSNs,such that all filters can reach a consensus on the estimate of sparse nonlinear signals.The simulation results show that the Optimized algorithm is effect.

关 键 词：无线传感器网络 卡尔曼一致滤波 压缩感知 重构算法 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]