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计算机工程 ›› 2020, Vol. 46 ›› Issue (12): 179-184. doi: 10.19678/j.issn.1000-3428.0056965

• 移动互联与通信技术 • 上一篇    下一篇

基于改进哈里斯鹰优化算法的TDOA定位

马一鸣1, 石志东1, 赵康2,3, 贡常磊1, 单联海2,4   

  1. 1. 上海大学 特种光纤与光接入网重点实验室, 上海 200444;
    2. 上海物联网有限公司, 上海 201899;
    3. 华东师范大学 计算机科学与软件工程学院, 上海 200062;
    4. 中国科学院上海微系统与信息技术研究所, 上海 200050
  • 收稿日期:2019-12-19 修回日期:2020-02-08 发布日期:2020-02-24
  • 作者简介:马一鸣(1992-),男,硕士研究生,主研方向为无线网络、室内定位技术;石志东(通信作者),研究员、博士;赵康,助理研究员、硕士;贡常磊,硕士研究生;单联海,副研究员、博士。
  • 基金资助:
    国家重点研发计划(2019YFB2101602);上海市科技创新行动计划项目(19511102900)。

TDOA Localization Based on Improved Harris Hawk Optimization Algorithm

MA Yiming1, SHI Zhidong1, ZHAO Kang2,3, GONG Changlei1, SHAN Lianhai2,4   

  1. 1. Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai University, Shanghai 200444, China;
    2. Shanghai Internet of Things Co., Ltd., Shanghai 201899, China;
    3. School of Computer Science and Software Engineering, East China Normal University, Shanghai 200062, China;
    4. Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China
  • Received:2019-12-19 Revised:2020-02-08 Published:2020-02-24

摘要: 针对室内到达时差(TDOA)定位的非线性方程求解问题,提出一种改进的哈里斯鹰优化定位算法,在提升原算法性能的基础上保留其寻优机制。对基于最大似然估计的适应度函数进行改进,在优化过程中达到更优的适应度值,从而提高算法的寻优精度。同时在初始种群位置中引入初始解,以减少不必要的全局搜索,在不影响种群多样性的前提下提高算法的收敛速度。仿真结果表明,与DHHO/M、EWOA、IALOT和CSSA算法相比,该算法具有更高的定位精度和收敛速度。

关键词: 室内定位, 到达时差, 智能优化算法, 哈里斯鹰优化算法, 适应度函数

Abstract: To solve the nonlinear equation problem of indoor Time Difference of Arrival(TDOA) localization,this paper proposes a localization algorithm based on improved Harris Hawk Optimization(HHO),maintaining the optimization mechanism while enhancing the performance of HHO.The proposed algorithm improves the fitness function based on maximum likelihood estimation to obtain better fitness value in the optimization process,which increases the optimization accuracy.Meanwhile,the initial solution is introduced into the initial population position,which reduces unnecessary global search and improves the convergence speed of the algorithm without affecting the population diversity.Simulation results show that,compared with DHHO/M,EWOA,IALOT and CSSA algorithms,the proposed algorithm has higher localization accuracy and convergence speed.

Key words: indoor localization, Time Difference of Arrival(TDOA), intelligent optimization algorithm, Harris Hawk Optimization(HHO) algorithm, fitness function

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