Evidence map›Paper›PMID 40807706›Full record

ArticleSensors (Basel, Switzerland)2025

A Localization Enhancement Method Based on Direct-Path Identification and Tracking for Future Networks.

Yuhong Huang, Youping Zhao

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Yuhong HuangSchool of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China.ORCID 0000-0001-6136-6864
Youping ZhaoSchool of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China.ORCID 0000-0001-5833-5725

Funding

National Key Research and Development Program of China 2021YFB3900801
6 · The paper itself

Abstract

Localization is one of the essential problems in the Internet of Things (IoT). Dynamic changes in the radio environment may lead to poor localization accuracy or discontinuous localization in non-line-of-sight (NLOS) scenarios. To address this problem, this paper proposes a localization enhancement method based on direct-path identification and tracking. More specifically, the proposed method significantly reduces the range error and localization error by quickly identifying the line-of-sight (LOS) to NLOS transition and effectively tracking the direct path. In a large testing hall, localization experiments based on the ultra-wideband (UWB) signal have been carried out. Experimental results show that the proposed method achieves a root mean square localization error of less than 0.3 m along the user equipment (UE) movement trajectory with serious NLOS propagation conditions. Compared with conventional methods, the proposed method significantly improves localization accuracy while ensuring continuous localization.

Indexed as

direct pathlocalizationlocation trackingnon-line-of-sight (NLOS)range errorultra-wideband (UWB)

Identifiers

PMID40807706
PMCPMC12349149

What OpenQuestion holds

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Registered trials

None linked

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.