LANDMARC indoor positioning algorithm based on density-based spatial clustering of applications with noise–genetic algorithm–radial basis function neural network

In recent years, fifth-generation communication technology has begun to experiment successfully. As an indoor positioning technology of the Internet of things, it changes with each passing day and shows great vitality in the development of smart cities. Aiming at the problem that existing radio freq...

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Main Authors: Jingqiu Ren, Ke Bao, Guanghua Zhang, Li Chu, Weidang Lu
Format: Article
Language:English
Published: Wiley 2020-02-01
Series:International Journal of Distributed Sensor Networks
Online Access:https://doi.org/10.1177/1550147720907831
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author Jingqiu Ren
Ke Bao
Guanghua Zhang
Li Chu
Weidang Lu
author_facet Jingqiu Ren
Ke Bao
Guanghua Zhang
Li Chu
Weidang Lu
author_sort Jingqiu Ren
collection DOAJ
description In recent years, fifth-generation communication technology has begun to experiment successfully. As an indoor positioning technology of the Internet of things, it changes with each passing day and shows great vitality in the development of smart cities. Aiming at the problem that existing radio frequency identification indoor positioning algorithm is prone to environmental interference and poor positioning accuracy, a LANDMARC indoor positioning algorithm based on density-based spatial clustering of applications with noise–genetic algorithm–radial basis function neural network is proposed. In this article, the signal intensity value is processed by Gaussian filter, and the noise points and boundary points are removed by density-based clustering algorithm. The threshold and weight of radial basis function neural network were optimized by genetic algorithm. With less data information, the relationship between the value of label signal strength and position coordinate could be established to improve the positioning accuracy of LANDMARC positioning algorithm. Experimental research shows that the average positioning error of the proposed LANDMARC algorithm based on density-based spatial clustering of applications with noise–genetic algorithm–radial basis function neural network is about 0.9 m, which is 64% lower than the average positioning error of the traditional LANDMARC algorithm and improves the indoor positioning accuracy.
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institution Kabale University
issn 1550-1477
language English
publishDate 2020-02-01
publisher Wiley
record_format Article
series International Journal of Distributed Sensor Networks
spelling doaj-art-85dd188fe31a42cbbe000bfffe07c4462025-02-03T05:44:34ZengWileyInternational Journal of Distributed Sensor Networks1550-14772020-02-011610.1177/1550147720907831LANDMARC indoor positioning algorithm based on density-based spatial clustering of applications with noise–genetic algorithm–radial basis function neural networkJingqiu Ren0Ke Bao1Guanghua Zhang2Li Chu3Weidang Lu4School of Electrical Engineering and Information, Northeast Petroleum University, Daqing, ChinaSchool of Electrical Engineering and Information, Northeast Petroleum University, Daqing, ChinaSchool of Electrical Engineering and Information, Northeast Petroleum University, Daqing, ChinaDaqing Oilfield Information Technology Company, Daqing, ChinaCollege of Information Engineering, Zhejiang University of Technology, Hangzhou, ChinaIn recent years, fifth-generation communication technology has begun to experiment successfully. As an indoor positioning technology of the Internet of things, it changes with each passing day and shows great vitality in the development of smart cities. Aiming at the problem that existing radio frequency identification indoor positioning algorithm is prone to environmental interference and poor positioning accuracy, a LANDMARC indoor positioning algorithm based on density-based spatial clustering of applications with noise–genetic algorithm–radial basis function neural network is proposed. In this article, the signal intensity value is processed by Gaussian filter, and the noise points and boundary points are removed by density-based clustering algorithm. The threshold and weight of radial basis function neural network were optimized by genetic algorithm. With less data information, the relationship between the value of label signal strength and position coordinate could be established to improve the positioning accuracy of LANDMARC positioning algorithm. Experimental research shows that the average positioning error of the proposed LANDMARC algorithm based on density-based spatial clustering of applications with noise–genetic algorithm–radial basis function neural network is about 0.9 m, which is 64% lower than the average positioning error of the traditional LANDMARC algorithm and improves the indoor positioning accuracy.https://doi.org/10.1177/1550147720907831
spellingShingle Jingqiu Ren
Ke Bao
Guanghua Zhang
Li Chu
Weidang Lu
LANDMARC indoor positioning algorithm based on density-based spatial clustering of applications with noise–genetic algorithm–radial basis function neural network
International Journal of Distributed Sensor Networks
title LANDMARC indoor positioning algorithm based on density-based spatial clustering of applications with noise–genetic algorithm–radial basis function neural network
title_full LANDMARC indoor positioning algorithm based on density-based spatial clustering of applications with noise–genetic algorithm–radial basis function neural network
title_fullStr LANDMARC indoor positioning algorithm based on density-based spatial clustering of applications with noise–genetic algorithm–radial basis function neural network
title_full_unstemmed LANDMARC indoor positioning algorithm based on density-based spatial clustering of applications with noise–genetic algorithm–radial basis function neural network
title_short LANDMARC indoor positioning algorithm based on density-based spatial clustering of applications with noise–genetic algorithm–radial basis function neural network
title_sort landmarc indoor positioning algorithm based on density based spatial clustering of applications with noise genetic algorithm radial basis function neural network
url https://doi.org/10.1177/1550147720907831
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AT lichu landmarcindoorpositioningalgorithmbasedondensitybasedspatialclusteringofapplicationswithnoisegeneticalgorithmradialbasisfunctionneuralnetwork
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