Hybrid Approach for Indoor Localization Using Received Signal Strength of Dual-Band Wi-Fi.

Byeong-Ho Lee, Kyoung-Min Park, Yong-Hwa Kim, Seong-Cheol Kim

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Optimal Configuration for Monitoring Stations in a Wireless Localisation Network Based on Received Signal Strength Differences.Sensors (Basel, Switzerland) · 2023
Article

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Byeong-Ho LeeDepartment of Electrical and Computer Engineering, Institute of New Media & Communications, Seoul National University (SNU), Seoul 08826, Korea.ORCID 0000-0001-8999-0861

Kyoung-Min ParkDepartment of Electrical and Computer Engineering, Institute of New Media & Communications, Seoul National University (SNU), Seoul 08826, Korea.ORCID 0000-0003-4927-8882

Yong-Hwa KimDepartment of Data Science, Korea National University of Transportation, Uiwang-si 16106, Korea.ORCID 0000-0003-2183-5085

Seong-Cheol KimDepartment of Electrical and Computer Engineering, Institute of New Media & Communications, Seoul National University (SNU), Seoul 08826, Korea.ORCID 0000-0002-7896-5625

Funding

Defense Acquisition Program Administration UD190018ID

6 · The paper itself

Abstract

In this paper, we propose a hybrid localization algorithm to boost the accuracy of range-based localization by improving the ranging accuracy under indoor non-line-of-sight (NLOS) conditions. We replaced the ranging part of the rule-based localization method with a deep regression model that uses data-driven learning with dual-band received signal strength (RSS). The ranging error caused by the NLOS conditions was effectively reduced by using the deep regression method. As a consequence, the positioning error could be reduced under NLOS conditions. The performance of the proposed method was verified through a ray-tracing-based simulation for indoor spaces. The proposed scheme showed a reduction in the positioning error of at least 22.3% in terms of the median root mean square error compared to the existing methods. In addition, we verified that the proposed method was robust to changes in the indoor structure.

Indexed as

dual-bandindoor localizationrange-based localizationreceived signal strengthtrilaterationWi-Fi

Identifiers

PMID34451026

DOI10.3390/s21165583

PMCPMC8402246

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.