基于粒子群优化的多径辅助室内定位算法

doi:10.3969/j.issn.0372-2112.2020.10.012

电子学报 ›› 2020, Vol. 48 ›› Issue (10): 1952-1960.DOI: 10.3969/j.issn.0372-2112.2020.10.012

所属专题：粒子群优化算法；定位技术新进展

基于粒子群优化的多径辅助室内定位算法

李泽, 田增山, 王中春, 王亚

重庆邮电大学通信与信息工程学院, 重庆 400065

收稿日期:2019-05-24 修回日期:2020-04-21 出版日期:2020-10-25
- 作者简介:
- 李泽男,1991年11月出生,安徽淮南人.现为重庆邮电大学信息与通信工程专业在读博士研究生,主要研究方向为无线定位.E-mail:lizecqupt@yahoo.com
  田增山 男,1968年12月出生,河南固始人.现为重庆邮电大学教授、博士生导师.主要研究方向为蜂窝网无线定位系统、数据压缩和数据融合.E-mail:tianzs@cqupt.edu.cn
- 基金资助:
- 国家自然科学基金 (No.61771083，No.61704015）; 重庆邮电大学博士研究生创新人才项目 (No.BYJS201805）

Multipath-Assisted Indoor Localization Algorithm Based on Particle Swarm Optimization

LI Ze, TIAN Zeng-shan, WANG Zhong-chun, WANG Ya

School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Received:2019-05-24 Revised:2020-04-21 Online:2020-10-25 Published:2020-10-25
- Supported by:
- National Natural Science Foundation of China (No.61771083, No.61704015); Innovative Talents Program for PhD Students of Chongqing University of Posts and Telecommunications (No.BYJS201805)

摘要/Abstract

摘要： 由于室内多径信号丰富且包含了室内几何信息，可以利用室内多径信号对目标进行定位.基于此，本文提出了一种多径辅助的目标定位算法.首先，利用多径信号的差分飞行时间（Time of Flight，TOF）构建关于目标以及散射体位置的适应度函数；然后，提出了基于粒子群优化（Particle Swarm Optimization，PSO）的目标及散射体位置联合搜索算法，其中利用目标及散射体到达角（Angle of Arrival，AOA）确定搜索范围；其次，选取搜索到的散射体位置联合差分TOF求解目标位置；最后，利用仿射传播聚类（Affinity Propagation Clustering，APC）对所有散射体估计到的目标位置进行聚类，提出聚类准则消除大的定位误差点.仿真结果表明，本文所提算法利用单个基站可以达到较高定位精度.

关键词: 室内定位, 多径信号, 粒子群优化, 仿射传播聚类

Abstract: Multipath signals can be used to realize localization since they are abundant and contain geometry information of indoor environments. Based on this, this paper proposes a multipath-assisted target localization algorithm. Firstly, the fitness function about the target and scatterer locations is constructed with Time of Flight (TOF) differences. Then, the locations of the target and scatterers are searched jointly by Particle Swarm Optimization (PSO) and Angle of Arrivals (AOAs) that determines searching ranges. Secondly, the estimated locations of scatterers and TOF differences are used to estimate the target location. Finally, all target locations are clustered by using Affinity Propagation Clustering (APC), and a clustering criterion is proposed to eliminate big localization errors. The simulation results show that the proposed algorithm can achieve high localization accuracy with a single base station.

Key words: indoor localization, multipath signal, particle swarm optimization, affinity propagation clustering

中图分类号:

TP391

李泽, 田增山, 王中春, 等. 基于粒子群优化的多径辅助室内定位算法[J]. 电子学报, 2020, 48(10): 1952-1960.

LI Ze, TIAN Zeng-shan, WANG Zhong-chun, et al. Multipath-Assisted Indoor Localization Algorithm Based on Particle Swarm Optimization[J]. Acta Electronica Sinica, 2020, 48(10): 1952-1960.

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基于粒子群优化的多径辅助室内定位算法

Multipath-Assisted Indoor Localization Algorithm Based on Particle Swarm Optimization

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