改进蝗虫优化-弱匹配追踪算法的WSN目标定位  

Improved Grasshopper Optimization-weak Matching Pursuit Algorithm for WSN Target Localization

作　　者：张小康 肖本贤[1] 马正祥 杨晓俊 ZHANG Xiaokang;XIAO Benxian;MA Zhengxiang;YANG Xiaojun(School of Electrical Engineering and Automation,Hefei University of Technology,Hefei 230009,China;Tianzhu Science and Technology Co.,Ltd.,Zhengzhou 450008,China)

机构地区：[1]合肥工业大学电气与自动化工程学院,安徽合肥230009 [2]天筑科技股份有限公司,河南郑州450008

出　　处：《仪表技术》2022年第4期37-42,共6页Instrumentation Technology

摘　　要：为了解决传统压缩感知算法(CS)在应用于无线传感器网络(WSN)定位时精度较低的问题,提出了一种改进蝗虫优化弱匹配追踪算法(IGO-WMP)。引入分段的思想,在不同的阶段通过阈值控制候选集原子的选取;利用改进的蝗虫算法优化弱匹配追踪算法的阈值参数。相较于传统的蝗虫优化算法(GOA),采用非线性权重参数和融入柯西差分变异策略后的蝗虫优化算法能够跳出局部最优解,以更高概率获得全局最优解。仿真实验结果表明,应用改进蝗虫优化弱匹配追踪算法的WSN定位,其定位精度优于传统的压缩感知算法。In order to solve the problem of low accuracy of traditional compressed sensing(CS) algorithm applied to wireless sensor network(WSN) localization,this paper proposes an improved grasshopper optimization-weak match pursuit(IGO-WMP) algorithm.The idea of segmentation is first introduced to control the selection of candidate set atoms through threshold at different stages,and then the improved grasshopper algorithm is used to optimize the threshold parameters of the weak match pursuit algorithm.Compared with the traditional grasshopper optimized algorithm(GOA),the GOA with the non-linear weight parameters and the incorporation of the Cauchy difference variation strategy can jump out of the local optimal solution and obtain the global optimal solution with a higher probability.The experimental results show that the localization accuracy of the IGO-WMP algorithm is better than that of the traditional compressed sensing algorithm when applied to WSN localization.

关 键 词：弱匹配 蝗虫优化算法 柯西差分变异 无线传感器网络 目标定位 

分 类 号：TP212.9[自动化与计算机技术—检测技术与自动化装置] TN911.73[自动化与计算机技术—控制科学与工程]