Multipath-Assisted Indoor Localization Algorithm Based on Particle Swarm Optimization

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

doi:10.3969/j.issn.0372-2112.2020.10.012

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Multipath-Assisted Indoor Localization Algorithm Based on Particle Swarm Optimization

更新时间：2023-02-24

- Multipath-Assisted Indoor Localization Algorithm Based on Particle Swarm Optimization
- Acta Electronica Sinica Vol. 48, Issue 10, Pages: 1952-1960(2020)
- 作者机构：
  
  重庆邮电大学通信与信息工程学院,重庆,400065
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2020.10.012
  CLC： TP391
- Published Online：25 October 2020，
  
  Published：2020
- 稿件说明：
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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.
DOI：

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. DOI： 10.3969/j.issn.0372-2112.2020.10.012.

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.

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.

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