Evidence map›Paper›PMID 30205490›Full record

ArticleSensors (Basel, Switzerland)2018

NLOS Identification and Positioning Algorithm Based on Localization Residual in Wireless Sensor Networks.

Jingyu Hua, Yejia Yin, Weidang Lu, Yu Zhang, Feng Li

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

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

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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

5 authors.

Jingyu HuaZhejiang Gongshang University, Hangzhou 310018, China. eehjy@163.com.ORCID 0000-0003-1377-6775
Yejia YinZhejiang University of Technology, Hangzhou 310023, China. 13758223512@163.com.
Weidang LuZhejiang University of Technology, Hangzhou 310023, China. luweid@zjut.edu.cn.ORCID 0000-0002-8919-0327
Yu ZhangZhejiang University of Technology, Hangzhou 310023, China. yzhang@zjut.edu.cn.
Feng LiZhejiang University of Technology, Hangzhou 310023, China. fenglzj@zjut.edu.cn.

Funding

National Natural Science Foundation of China 61471322
6 · The paper itself

Abstract

The problem of target localization in WSN (wireless sensor network) has received much attention in recent years. However, the performance of traditional localization algorithms will drastically degrade in the non-line of sight (NLOS) environment. Moreover, variable methods have been presented to address this issue, such as the optimization-based method and the NLOS modeling method. The former produces a higher complexity and the latter is sensitive to the propagating environment. Therefore, this paper puts forward a simple NLOS identification and localization algorithm based on the residual analysis, where at least two line-of-sight (LOS) propagating anchor nodes (AN) are required. First, all ANs are grouped into several subgroups, and each subgroup can get intermediate position estimates of target node through traditional localization algorithms. Then, the AN with an NLOS propagation, namely NLOS-AN, can be identified by the threshold based hypothesis test, where the test variable, i.e., the localization residual, is computed according to the intermediate position estimations. Finally, the position of target node can be estimated by only using ANs under line of sight (LOS) propagations. Simulation results show that the proposed algorithm can successfully identify the NLOS-AN, by which the following localization produces high accuracy so long as there are no less than two LOS-ANs.

Indexed as

localization residualnon-line-of-sight errorwireless localizationwireless sensor network

Identifiers

PMID30205490
PMCPMC6165043

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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.