改进的免疫粒子群算法在TDOA定位中的应用  

Application of Improved Immune Particle Swarm Optimization in TDOA Localization Estimation

作　　者：王田 兰艳亭 郭译凡 李大威 牛兴龙 WANG Tian;LAN Yanting;GUO Yifan;LI Dawei;NIU Xinglong(School of Electrical and Control Engineering,North University of China,Taiyuan 030051,China)

机构地区：[1]中北大学电气与控制工程学院,山西太原030051

出　　处：《无线电工程》2023年第5期1199-1206,共8页Radio Engineering

基　　金：山西省基础研究计划资助项目(20210302123026);中北大学第18届研究生科技立项项目(20210502)。

摘　　要：针对无线传感器网络中的TDOA节点无源定位估计中的非线性优化问题,提出了一种改进的免疫粒子群优化(Immune Particle Swarm Optimization, IPSO)的TDOA定位算法。该算法在自适应粒子群算法的基础上,引入免疫过程,增加了粒子种群的多样性,平衡局部搜索能力和全局搜索能力,有效地解决粒子易陷入局部最优问题,更快收敛到全局最优解。仿真结果表明,提出的算法相比于标准粒子群算法、自适应粒子群算法、Chan算法,当基站数量仅为4~5个、半径达到100 m时定位精度仍然较高,当加入随机噪声时,性能更加稳定,鲁棒性较好。To solve the nonlinear optimization problem of passive localization estimation of TDOA nodes in wireless sensor networks,an improved TDOA localization algorithm based on Immune Particle Swarm Optimization(IPSO)is proposed.Based on the self-adaptive particle swarm optimization algorithm,the immune process is introduced,which increases the diversity of particle population,balances the local search ability and the global search ability,effectively solves the problem that particles are easy to fall into the local optimal solution and converges to the global optimal solution more quickly.The simulation results show that the proposed algorithm is more stable and robust than the standard particle swarm optimization algorithm,self-adaptive particle swarm optimization algorithm and Chan algorithm when the number of base stations is only 4~5 and the radius reaches 100 m.When random noise is added,the performance is more stable and robust.

关 键 词：时差定位 最大似然估计 非线性优化 免疫粒子群算法 

分 类 号：TP312[自动化与计算机技术—计算机软件与理论]