Evidence map›Paper›PMID 36146157›Full record

ArticleSensors (Basel, Switzerland)2022

Wireless Sensor Network-Based Rigid Body Localization for NLOS Parameter Estimation.

Pengwu Wan, Jian Wei, Jin Wang, Qiongdan Huang

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. 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

4 authors.

Pengwu WanSchool of Communications and Information Engineering & School of Artificial Intelligence, Xi'an University of Posts and Telecommunications, Xi'an 710121, China.ORCID 0000-0002-6118-4291
Jian WeiSchool of Communications and Information Engineering & School of Artificial Intelligence, Xi'an University of Posts and Telecommunications, Xi'an 710121, China.ORCID 0000-0001-8317-525X
Jin WangSchool of Communications and Information Engineering & School of Artificial Intelligence, Xi'an University of Posts and Telecommunications, Xi'an 710121, China.
Qiongdan HuangSchool of Communications and Information Engineering & School of Artificial Intelligence, Xi'an University of Posts and Telecommunications, Xi'an 710121, China.

Funding

Key Research and Development Program of Shaanxi in Industrial Domain 2021GY-103National Natural Science Foundation of China 62101441
6 · The paper itself

Abstract

In wireless sensor network (WSN)-based rigid body localization (RBL) systems, the non-line-of-sight (NLOS) propagation of the wireless signals leads to severe performance deterioration. This paper focuses on the RBL problem under the NLOS environment based on the time of arrival (TOA) measurement between the sensors fixed on the rigid body and the anchors, where the NLOS parameters are estimated to improve the RBL performance. Without any prior information about the NLOS environment, the highly non-linear and non-convex RBL problem is transformed into a difference of convex (DC) programming, which can be solved by using the concave-convex procedure (CCCP) to determine the position of the rigid body sensors and the NLOS parameters. To avoid error accumulation, the obtained NLOS parameters are utilized to refine the localization performance of the rigid body sensors. Then, the accurate position and the orientation of the rigid body in two-Dimensional space are obtained according to the relative deflection angle method. To reduce the computational complexity, the singular value decomposition (SVD) method is employed to solve the problem in three-Dimensional space. Simulation results show that the proposed method can effectively improve the performance of the rigid body localization based on the wireless sensor network in NLOS environment.

Indexed as

concave–convex procedurenon-line-of-sightrigid body localizationtime of arrival

Identifiers

PMID36146157
PMCPMC9504766

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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.