Indoor Localization Algorithm for NLOS Environment

MAO Ke-ji; WU Jin-bin; JIN Hong-bo; MIAO Chun-yu; XIA Ming; CHEN Qing-zhang

doi:10.3969/j.issn.0372-2112.2016.05.023

您当前的位置：

首页 >

文章列表页 >

Indoor Localization Algorithm for NLOS Environment

更新时间：2025-07-16

- Indoor Localization Algorithm for NLOS Environment
- Acta Electronica Sinica Vol. 44, Issue 5, Pages: 1174-1179(2016)
- 作者机构：
  
  1. 浙江工业大学计算机科学与技术学院,浙江,杭州,310023
  2. 浙江师范大学行知学院,浙江,金华,321004
  3. 浙江工业大学计算机科学与技术学院,浙江,杭州,310023
  4. 浙江师范大学行知学院,浙江,金华,321004
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2016.05.023
  CLC： TN915.9
- Published Online：25 May 2016，
  
  Published：2016
- 稿件说明：
移动端阅览
MAO Ke-ji, WU Jin-bin, JIN Hong-bo, et al. Indoor Localization Algorithm for NLOS Environment[J]. Acta Electronica Sinica, 2016, 44(5): 1174-1179.
DOI：

MAO Ke-ji, WU Jin-bin, JIN Hong-bo, et al. Indoor Localization Algorithm for NLOS Environment[J]. Acta Electronica Sinica, 2016, 44(5): 1174-1179. DOI： 10.3969/j.issn.0372-2112.2016.05.023.

摘要

节点位置信息在无线传感器网络中起着至关重要的作用.大多数定位算法在视距(Line-of-Sight

LOS)环境下能够取得较高的定位精度

然而在非视距(Non-Line-of-Sight

NLOS)环境下

由于障碍物的阻挡

无法取得理想的定位精度.针对室内环境中普遍存在的非视距传播现象

提出了基于RTT(Round Trip Time)和AOA(Angle Of Arrival)混合测距方式的室内定位方法

一种轻量级基于网格的聚类算法(Lightweight Grid-Based Cluster

LGBC)被用来生成移动节点的定位区域.算法不需要获取室内环境的先验信息.仿真结果表明

LGBC算法复杂度低

计算开销小

并且与同类算法相比

定位精度提高约65%.

Abstract

Location of sensor plays a pivot role in WSNs.Most of the localization algorithms can achieve extremely high positioning accuracy in line of sight (LOS) environment.However

they are unable to obtain ideal accuracy due to the obstacles in non-line of sight (NLOS) environment.In order to solve the NLOS propagation problem in indoor environment

we propose an indoor localization method based on RTT and AOA using a lightweight grid-based clustering (LGBC) algorithm.The LGBC algorithm does not depend on any prior information of indoor environment and possesses significant flexibility.The simulation results show that LGBC algorithm has low time complexity and small computational overhead.Furthermore

it outperforms the other method by about 65 percent in terms of localization accuracy.

关键词

Keywords

references

Views

771

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

A Temporal-Pulse Amplitude Clustering-Based Unknown Radar Emitter Signal Sorting Method

Continuous Relation Extraction Technique Based on Dynamic Relational Prototypes

Pseudo-label Denoising and SAM Optimization for Large-scale Unsupervised Semantic Segmentation

Fingerprinting Inference Model Calibration Towards Visible Light-Based Positioning

Heuristic Clustering Algorithm Based on Sub-Game Perfect Equilibrium

Related Author

LIN Jin-jian

HUANG Ming-jun

SUN Hui-bo

LIN Zi-han

XIE Kai

ZHONG Jiang

DAI Qi-zhu

LI Xue

Related Institution

The 29th Research Institute of China Electronics Technology Group Corporation

Deptment of Electronics and Communication Engineering, Sun Yat-sen University of China

School of Electrical Engineering and Computer Science, The University of Queensland

Key Laboratory of Dependable Service Computing in Cyber Physical Society, Chongqing University

School of Computer Science, Chongqing University

⁰