RSS-Based Indoor Localization Using Min-Max Algorithm With Area Partition Strategy

Min-Max algorithm was widely used as a simple received signal strength (RSS-) based algorithm for indoor localization due to its easy implementation. However, the original Min-Max algorithm only achieves coarse estimation in which the target node (TN) is regarded as the geometric centroid of the are...

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Main Authors: Kuo Yang, Zhonghua Liang, Ren Liu, Wei Li
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9534746/
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spelling doaj-0485da01770b4f8ea16dc472985331b22021-09-16T23:00:22ZengIEEEIEEE Access2169-35362021-01-01912556112556810.1109/ACCESS.2021.31116509534746RSS-Based Indoor Localization Using Min-Max Algorithm With Area Partition StrategyKuo Yang0https://orcid.org/0000-0003-3111-5639Zhonghua Liang1https://orcid.org/0000-0002-7200-6685Ren Liu2Wei Li3https://orcid.org/0000-0001-5928-1220Department of Communication Engineering, School of Information Engineering, Chang’an University, Xi’an, ChinaDepartment of Communication Engineering, School of Information Engineering, Chang’an University, Xi’an, ChinaDepartment of Communication Engineering, School of Information Engineering, Chang’an University, Xi’an, ChinaDepartment of Communication Engineering, School of Information Engineering, Chang’an University, Xi’an, ChinaMin-Max algorithm was widely used as a simple received signal strength (RSS-) based algorithm for indoor localization due to its easy implementation. However, the original Min-Max algorithm only achieves coarse estimation in which the target node (TN) is regarded as the geometric centroid of the area of interest determined by measured RSS values. Although extended Min-Max (E-Min-Max) methods using weighted centroid instead of geometric centroid were recently proposed to cope with this problem, the improvement in the localization accuracy is still limited. In this paper, an improved Min-Max algorithm with area partition strategy (Min-Max-APS) is proposed to achieve better localization performance. In the proposed algorithm, the area of interest is first partitioned into four subareas, each of which contains a vertex of the original area of interest. Moreover, a minimum range difference criterion is designed to determine the target affiliated subarea whose vertex is “closest” to the target node. Then the target node’s location is estimated as the weighted centroid of the target affiliated subarea. Since the target affiliated subarea is smaller than the original area of interest, the weighted centroid of the target affiliated subarea will be more accurate than that of the original area of interest. Simulation results show that the localization error (LE) of the proposed Min-Max-APS algorithm can drop below 0.16 meters, which is less than one-half of that of the E-Min-Max algorithm, and is also less than one-seventh of that of the original Min-Max algorithm. Moreover, for the proposed Min-Max-APS, 90% of the LE are smaller than 0.38 meters, while the same percentage of the LE are as high as 0.49 meters for the E-Min-Max and 1.12 meters for the original Min-Max, respectively.https://ieeexplore.ieee.org/document/9534746/Min-Max algorithmreceived signal strength (RSS)area partitionindoor localizationtarget node (TN)
collection DOAJ
language English
format Article
sources DOAJ
author Kuo Yang
Zhonghua Liang
Ren Liu
Wei Li
spellingShingle Kuo Yang
Zhonghua Liang
Ren Liu
Wei Li
RSS-Based Indoor Localization Using Min-Max Algorithm With Area Partition Strategy
IEEE Access
Min-Max algorithm
received signal strength (RSS)
area partition
indoor localization
target node (TN)
author_facet Kuo Yang
Zhonghua Liang
Ren Liu
Wei Li
author_sort Kuo Yang
title RSS-Based Indoor Localization Using Min-Max Algorithm With Area Partition Strategy
title_short RSS-Based Indoor Localization Using Min-Max Algorithm With Area Partition Strategy
title_full RSS-Based Indoor Localization Using Min-Max Algorithm With Area Partition Strategy
title_fullStr RSS-Based Indoor Localization Using Min-Max Algorithm With Area Partition Strategy
title_full_unstemmed RSS-Based Indoor Localization Using Min-Max Algorithm With Area Partition Strategy
title_sort rss-based indoor localization using min-max algorithm with area partition strategy
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2021-01-01
description Min-Max algorithm was widely used as a simple received signal strength (RSS-) based algorithm for indoor localization due to its easy implementation. However, the original Min-Max algorithm only achieves coarse estimation in which the target node (TN) is regarded as the geometric centroid of the area of interest determined by measured RSS values. Although extended Min-Max (E-Min-Max) methods using weighted centroid instead of geometric centroid were recently proposed to cope with this problem, the improvement in the localization accuracy is still limited. In this paper, an improved Min-Max algorithm with area partition strategy (Min-Max-APS) is proposed to achieve better localization performance. In the proposed algorithm, the area of interest is first partitioned into four subareas, each of which contains a vertex of the original area of interest. Moreover, a minimum range difference criterion is designed to determine the target affiliated subarea whose vertex is “closest” to the target node. Then the target node’s location is estimated as the weighted centroid of the target affiliated subarea. Since the target affiliated subarea is smaller than the original area of interest, the weighted centroid of the target affiliated subarea will be more accurate than that of the original area of interest. Simulation results show that the localization error (LE) of the proposed Min-Max-APS algorithm can drop below 0.16 meters, which is less than one-half of that of the E-Min-Max algorithm, and is also less than one-seventh of that of the original Min-Max algorithm. Moreover, for the proposed Min-Max-APS, 90% of the LE are smaller than 0.38 meters, while the same percentage of the LE are as high as 0.49 meters for the E-Min-Max and 1.12 meters for the original Min-Max, respectively.
topic Min-Max algorithm
received signal strength (RSS)
area partition
indoor localization
target node (TN)
url https://ieeexplore.ieee.org/document/9534746/
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