基于分布式传感器的地下浅层震源定位  

Location of Underground Shallow Vibration Source Based on Distributed Sensors

作　　者：裴志鹏 李剑[1] 李晋 张鑫[1] 马翊翔 臧丹枫 PEI Zhipeng;LI Jian;LI Jin;ZHANG Xin;MA Yixiang;ZANG Danfeng(State Key Laboratory of Dynamic Testing Technology,Collaborative Innovation Center,North University,Taiyuan 030051,China)

机构地区：[1]中北大学省部共建动态测试技术国家重点实验室,太原030051

出　　处：《计算机测量与控制》2025年第1期317-324,共8页Computer Measurement &Control

基　　金：2024年山西省专利转化计划项目(200405004);国家自然基金面上科学基金(62271453);中央支持地方项目(YDZJSX2024D031);山西省青年学术带头人项目(2024Q022)。

摘　　要：针对无线传感器网络中的(TDOA)节点无源定位中的非线性优化问题,提出一种基于双种群粒子群(DIR-WOA)的鲸鱼优化的TDOA定位算法;在标准鲸鱼算法的基础上,引入混沌模型,改进粒子种群生成方式,利用主辅粒子群进行相互制约,有效解决粒子易陷入局部最优的问题,迅速得到全局最优解;仿真结果表明,改进算法相较于标准鲸鱼算法,增强了粒子搜索范围,算法收敛时间减少了0.568 8 s,定位误差减小到0.029 m。To solve nonlinear optimization of time difference of arrival(TDOA)node passive location in wireless sensor networks,a whale optimization TDOA location algorithm based on dual population particle swarm(DIR-WOA)is proposed.On the basis of the standard whale algorithm,the chaotic model is introduced to improve the particle population generation mode,and constrains the main and auxiliary particle swarm to each other,effectively solving the problems of particles easily falling into local optima and quickly obtaining global optima.Simulation results show that compared with the standard whale algorithm,the improved algorithm greatly enhances the search range of particle swarm,and reduces the convergence time by 0.5688 s and the positioning error to 0.029 m.

关 键 词：双种群粒子群 非线性优化 时差定位 混沌模型 鲸鱼算法 

分 类 号：TP2[自动化与计算机技术—检测技术与自动化装置]