A Multifilter Location Optimization Algorithm Based on Neural Network in LOS/NLOS Mixed Environment

While the modern communication system, embedded system, and sensor technology have been widely used at the moment, the wireless sensor network (WSN) composed of microdistributed sensors is favored due to its relatively excellent communication interaction, real-time computing, and sensing capabilitie...

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Autores principales:	Zhentian Bian, Long Cheng, Yan Wang
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Technology (General) T1-995
Acceso en línea:	https://doaj.org/article/b611375184c84fec98aff802a2bad73d
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spelling	oai:doaj.org-article:b611375184c84fec98aff802a2bad73d2021-11-22T01:11:16ZA Multifilter Location Optimization Algorithm Based on Neural Network in LOS/NLOS Mixed Environment1687-726810.1155/2021/6125890https://doaj.org/article/b611375184c84fec98aff802a2bad73d2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/6125890https://doaj.org/toc/1687-7268While the modern communication system, embedded system, and sensor technology have been widely used at the moment, the wireless sensor network (WSN) composed of microdistributed sensors is favored due to its relatively excellent communication interaction, real-time computing, and sensing capabilities. Because GPS positioning technology cannot meet the needs of indoor positioning, positioning based on WSN has become the better option for indoor localization. In the field of WSN indoor positioning, how to cope with the impact of NLOS error on positioning is still a big problem to be solved. In order to mitigate the influence of NLOS errors, a Neural Network Modified Multiple Filter Localization (NNMML) algorithm is proposed in this paper. In this algorithm, LOS and NLOS cases are distinguished firstly. Then, KF and UKF are applied in the LOS case and the NLOS case, respectively, and appropriate grouping processing is carried out for NLOS data. Finally, the positioning results after multiple filtering are corrected by neural network. The simulation results illustrate that the location accuracy of NNMML algorithm is better than that of KF, EKF, UKF, and the version without neural network correction. It also shows that NNMML is suitable for the situation with large NLOS error.Zhentian BianLong ChengYan WangHindawi LimitedarticleTechnology (General)T1-995ENJournal of Sensors, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Technology (General) T1-995
spellingShingle	Technology (General) T1-995 Zhentian Bian Long Cheng Yan Wang A Multifilter Location Optimization Algorithm Based on Neural Network in LOS/NLOS Mixed Environment
description	While the modern communication system, embedded system, and sensor technology have been widely used at the moment, the wireless sensor network (WSN) composed of microdistributed sensors is favored due to its relatively excellent communication interaction, real-time computing, and sensing capabilities. Because GPS positioning technology cannot meet the needs of indoor positioning, positioning based on WSN has become the better option for indoor localization. In the field of WSN indoor positioning, how to cope with the impact of NLOS error on positioning is still a big problem to be solved. In order to mitigate the influence of NLOS errors, a Neural Network Modified Multiple Filter Localization (NNMML) algorithm is proposed in this paper. In this algorithm, LOS and NLOS cases are distinguished firstly. Then, KF and UKF are applied in the LOS case and the NLOS case, respectively, and appropriate grouping processing is carried out for NLOS data. Finally, the positioning results after multiple filtering are corrected by neural network. The simulation results illustrate that the location accuracy of NNMML algorithm is better than that of KF, EKF, UKF, and the version without neural network correction. It also shows that NNMML is suitable for the situation with large NLOS error.
format	article
author	Zhentian Bian Long Cheng Yan Wang
author_facet	Zhentian Bian Long Cheng Yan Wang
author_sort	Zhentian Bian
title	A Multifilter Location Optimization Algorithm Based on Neural Network in LOS/NLOS Mixed Environment
title_short	A Multifilter Location Optimization Algorithm Based on Neural Network in LOS/NLOS Mixed Environment
title_full	A Multifilter Location Optimization Algorithm Based on Neural Network in LOS/NLOS Mixed Environment
title_fullStr	A Multifilter Location Optimization Algorithm Based on Neural Network in LOS/NLOS Mixed Environment
title_full_unstemmed	A Multifilter Location Optimization Algorithm Based on Neural Network in LOS/NLOS Mixed Environment
title_sort	multifilter location optimization algorithm based on neural network in los/nlos mixed environment
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/b611375184c84fec98aff802a2bad73d
work_keys_str_mv	AT zhentianbian amultifilterlocationoptimizationalgorithmbasedonneuralnetworkinlosnlosmixedenvironment AT longcheng amultifilterlocationoptimizationalgorithmbasedonneuralnetworkinlosnlosmixedenvironment AT yanwang amultifilterlocationoptimizationalgorithmbasedonneuralnetworkinlosnlosmixedenvironment AT zhentianbian multifilterlocationoptimizationalgorithmbasedonneuralnetworkinlosnlosmixedenvironment AT longcheng multifilterlocationoptimizationalgorithmbasedonneuralnetworkinlosnlosmixedenvironment AT yanwang multifilterlocationoptimizationalgorithmbasedonneuralnetworkinlosnlosmixedenvironment
_version_	1718418302075666432

A Multifilter Location Optimization Algorithm Based on Neural Network in LOS/NLOS Mixed Environment

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