DV-Hop定位算法的混沌粒子群优化  

Chaotic Particle Swarm Optimization Based on DV-Hop Localization Algorithm

作　　者：蒋建峰 丁慧洁[3] 张运嵩 蒋建锋 张娴[2] 卢晨[2] JIANG Jianfeng;DING Huijie;ZHANG Yunsong;JIANG Jianfeng;ZHANG Xian;LU Chen(School of Computer Science,Nanjing University of Posts and Telecommunications,Nanjing 210023,China;School of Information Engineering,Suzhou Industrial Park Institute of Services Outsourcing,Suzhou 215123,China;School of Artificial Intelligence,Guangdong Open University,Guangzhou 510091,China)

机构地区：[1]南京邮电大学计算机学院,江苏南京210023 [2]苏州工业园区服务外包职业学院信息工程学院,江苏苏州215123 [3]广东开放大学人工智能学院,广东广州510091

出　　处：《无线电工程》2023年第6期1430-1437,共8页Radio Engineering

基　　金：教育部科技发展中心2021年中国高校产学研创新基金(2021ALA03005);江苏省博士后研究基金(2018K009B);江苏省专业带头人高端研修项目(2020GRFX074);江苏省青蓝工程项目(202010)。

摘　　要：针对距离矢量跳距(Distance Vector Hop,DV-Hop)定位算法通信半径选择不合理导致平均跳距和定位误差较大的问题,提出一种基于混沌粒子群改进的DV-Hop定位算法,利用混沌映射的遍历性和随机性实现粒子的局部深度搜索,避免粒子群算法陷入局部最优。通过混沌粒子群优化(Particle Swarm Optimization,PSO)算法迭代求解所有信标节点的通信半径,引入混沌理论调整非线性惯性权重优化搜索过程,通过混沌搜索和混沌扰动迭代求解信标节点的最佳通信半径;通过极大似然估计(Maximum Likelihood Estimate,MLE)法计算的平均定位误差作为混沌粒子群算法的适应值函数;使用费希尔矩阵求解的误差下限作为约束条件求解适应值函数,同时把平均通信半径作为节点能耗模型的阈值来降低节点能量消耗。仿真实验表明,提出的算法在不增加算法复杂度的前提下能够在定位精度方面提升近58%,节点能量消耗方面降低近24%。To solve the problem of unreasonable selection of communication radius in Distance Vector Hop(DV-Hop)localization algorithm,which leads to large average hop distance and localization error,an improved DV-Hop localization algorithm based on chaotic particle swarm is proposed.The improved algorithm utilizes the ergodicity and randomness of the chaotic map to realize the local depth search of particles,so as to avoid the particle swarm algorithm from falling into the local optimum.First,the communication radius of all beacon nodes is iteratively solved by the chaotic Particle Swarm Optimization(PSO)algorithm,and chaos theory is introduced to adjust the nonlinear inertia weight to optimize the search process.The optimal communication radius of the beacon node is calculated through chaotic search and chaotic perturbation iteration.Secondly,the average positioning error calculated by the Maximum Likelihood Estimate(MLE)method is used as the fitness value function of the chaotic particle swarm algorithm.Finally,the fitness value function is solved by using the lower bound of the error solved by Fisher matrix as the constraint condition,and the average communication radius is used as the threshold of the node energy consumption model to reduce the node energy consumption.Simulation experiments show that the proposed algorithm can improve the positioning accuracy by nearly 58%and reduce the node energy consumption by nearly 24%without increasing the complexity of the algorithm.

关 键 词：无线传感网 距离矢量跳距 混沌计算 粒子群优化算法 极大似然估计 费希尔矩阵 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]