Evidence map›Paper›PMID 32443394›Full record

ArticleSensors (Basel, Switzerland)2020

Fingerprinting-Based Indoor Localization Using Interpolated Preprocessed CSI Phases and Bayesian Tracking.

Wenxu Wang, Damián Marelli, Minyue Fu

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

8 citing papers in PubMed.

  1. Review
  2. Article
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  7. Sensor Signal and Information Processing III.Sensors (Basel, Switzerland) · 2020
    Article
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4 · The record

Corrections and comments

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

3 authors.

Wenxu WangSchool of Automation, Guangdong University of Technology, Guangzhou 510006, China.ORCID 0000-0001-9323-7012
Damián MarelliSchool of Automation, Guangdong University of Technology, Guangzhou 510006, China.
Minyue FuSchool of Automation, Guangdong University of Technology, Guangzhou 510006, China.

Funding

Argentinean Agency for Scientific and Technological Promotion PICT- 201-0985Guangdong Basic and Applied Basic Research Foundation 2020A1515011505National Natural Science Foundation of China 61633014National Natural Science Foundation of China 61803101National Natural Science Foundation of China U1701264
6 · The paper itself

Abstract

Indoor positioning using Wi-Fi signals is an economic technique. Its drawback is that multipath propagation distorts these signals, leading to an inaccurate localization. An approach to improve the positioning accuracy consists of using fingerprints based on channel state information (CSI). Following this line, we propose a new positioning method which consists of three stages. In the first stage, which is run during initialization, we build a model for the fingerprints of the environment in which we do localization. This model permits obtaining a precise interpolation of fingerprints at positions where a fingerprint measurement is not available. In the second stage, we use this model to obtain a preliminary position estimate based only on the fingerprint measured at the receiver's location. Finally, in the third stage, we combine this preliminary estimation with the dynamical model of the receiver's motion to obtain the final estimation. We compare the localization accuracy of the proposed method with other rival methods in two scenarios, namely, when fingerprints used for localization are similar to those used for initialization, and when they differ due to alterations in the environment. Our experiments show that the proposed method outperforms its rivals in both scenarios.

Indexed as

Bayesian trackingCSIfingerprintingindoor localization

Identifiers

PMID32443394
PMCPMC7287928

What OpenQuestion holds

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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.