A study on the cabin indoor localization algorithm based on adaptive -values

In ocean voyages, cabin indoor localization plays a considerably important role in ship safety management. Due to the special structure of ships and the influence of internal and external environment, the mainstream indoor positioning technology nowadays is not ideal for positioning inside the cabin...

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Main Authors: Junbo Gao, Tao Fan, Zhenxiao Qin, Wei Sun
Format: Article
Language:English
Published: Wiley 2022-02-01
Series:International Journal of Distributed Sensor Networks
Online Access:https://doi.org/10.1177/15501477211073044
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author Junbo Gao
Tao Fan
Zhenxiao Qin
Wei Sun
author_facet Junbo Gao
Tao Fan
Zhenxiao Qin
Wei Sun
author_sort Junbo Gao
collection DOAJ
description In ocean voyages, cabin indoor localization plays a considerably important role in ship safety management. Due to the special structure of ships and the influence of internal and external environment, the mainstream indoor positioning technology nowadays is not ideal for positioning inside the cabin. In order to improve the positioning accuracy, this article proposes an adaptive K -value-based cabin indoor positioning algorithm. The algorithm constructs a fingerprint library by collecting Received Signal Strength Indication data and carries out filtering; matches fingerprint information by adaptive K -values during the localization process, while using double K -nearest neighbor to reduce the influence of outlier points on the final localization; and finally uses the multi-point mass center method to determine the final localization results. The experimental results in the automated cabin laboratory show that the average positioning error of the algorithm is 1.74 m, which can well meet the cabin indoor positioning requirements.
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institution DOAJ
issn 1550-1477
language English
publishDate 2022-02-01
publisher Wiley
record_format Article
series International Journal of Distributed Sensor Networks
spelling doaj-art-ec751aa651534e88af797df2e45f14ce2025-08-20T03:21:24ZengWileyInternational Journal of Distributed Sensor Networks1550-14772022-02-011810.1177/15501477211073044A study on the cabin indoor localization algorithm based on adaptive -valuesJunbo GaoTao FanZhenxiao QinWei SunIn ocean voyages, cabin indoor localization plays a considerably important role in ship safety management. Due to the special structure of ships and the influence of internal and external environment, the mainstream indoor positioning technology nowadays is not ideal for positioning inside the cabin. In order to improve the positioning accuracy, this article proposes an adaptive K -value-based cabin indoor positioning algorithm. The algorithm constructs a fingerprint library by collecting Received Signal Strength Indication data and carries out filtering; matches fingerprint information by adaptive K -values during the localization process, while using double K -nearest neighbor to reduce the influence of outlier points on the final localization; and finally uses the multi-point mass center method to determine the final localization results. The experimental results in the automated cabin laboratory show that the average positioning error of the algorithm is 1.74 m, which can well meet the cabin indoor positioning requirements.https://doi.org/10.1177/15501477211073044
spellingShingle Junbo Gao
Tao Fan
Zhenxiao Qin
Wei Sun
A study on the cabin indoor localization algorithm based on adaptive -values
International Journal of Distributed Sensor Networks
title A study on the cabin indoor localization algorithm based on adaptive -values
title_full A study on the cabin indoor localization algorithm based on adaptive -values
title_fullStr A study on the cabin indoor localization algorithm based on adaptive -values
title_full_unstemmed A study on the cabin indoor localization algorithm based on adaptive -values
title_short A study on the cabin indoor localization algorithm based on adaptive -values
title_sort study on the cabin indoor localization algorithm based on adaptive values
url https://doi.org/10.1177/15501477211073044
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