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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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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