Evidence map›Paper›PMID 30126251›Full record

ArticleSensors (Basel, Switzerland)2018

AOA-Based Three-Dimensional Multi-Target Localization in Industrial WSNs for LOS Conditions.

Ruonan Zhang, Jiawei Liu, Xiaojiang Du, Bin Li, Mohsen Guizani

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

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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

5 authors.

Ruonan ZhangDepartment of Communication Engineering, Northwestern Polytechnical University, Xi'an 710072, China. rzhang@nwpu.edu.cn.ORCID 0000-0003-0030-6758
Jiawei LiuDepartment of Communication Engineering, Northwestern Polytechnical University, Xi'an 710072, China. ljw0823@mail.nwpu.edu.cn.
Xiaojiang DuDepartment of Computer and Information Sciences, Temple University, Philadelphia, PA 19122, USA. dxj@ieee.org.
Bin LiDepartment of Communication Engineering, Northwestern Polytechnical University, Xi'an 710072, China. libin@nwpu.edu.cn.
Mohsen GuizaniDepartment of Electrical and Computer Engineering, University of Idaho, Moscow, ID 83844, USA. mguizani@ieee.org.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

High-precision and fast relative positioning of a large number of mobile sensor nodes (MSNs) is crucial for smart industrial wireless sensor networks (SIWSNs). However, positioning multiple targets simultaneously in three-dimensional (3D) space has been less explored. In this paper, we propose a new approach, called Angle-of-Arrival (AOA) based Three-dimensional Multi-target Localization (ATML). The approach utilizes two anchor nodes (ANs) with antenna arrays to receive the spread spectrum signals broadcast by MSNs. We design a multi-target single-input-multiple-output (MT-SIMO) signal transmission scheme and a simple iterative maximum likelihood estimator (MLE) to estimate the 2D AOAs of multiple MSNs simultaneously. We further adopt the skew line theorem of 3D geometry to mitigate the AOA estimation errors in determining locations. We have conducted extensive simulations and also developed a testbed of the proposed ATML. The numerical and field experiment results have verified that the proposed ATML can locate multiple MSNs simultaneously with high accuracy and efficiency by exploiting the spread spectrum gain and antenna array gain. The ATML scheme does not require extra hardware or synchronization among nodes, and has good capability in mitigating interference and multipath effect in complicated industrial environments.

Indexed as

angle of arrivalantenna array.localizationmaximum likelihood estimatorsmart industrialwireless sensor network

Identifiers

PMID30126251
PMCPMC6111292

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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.