Evidence map›Paper›PMID 34884032›Full record

ArticleSensors (Basel, Switzerland)2021

A Distance Increment Smoothing Method and Its Application on the Detection of NLOS in the Cooperative Positioning.

Dongqing Zhao, Dongmin Wang, Minzhi Xiang, Jinfei Li, Chaoyong Yang, Letian Zhang, Linyang Li

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2021. 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

7 authors.

Dongqing ZhaoInstitute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China.ORCID 0000-0001-8615-2459
Dongmin WangInstitute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China.
Minzhi XiangInstitute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China.
Jinfei LiInstitute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China.
Chaoyong YangInstitute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China.
Letian ZhangInstitute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China.
Linyang LiInstitute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China.

Funding

National Natural Science Foundation of China 41774037National Natural Science Foundation of China 42104033the key specialized research and development breakthrough in Henan province 212102310428
6 · The paper itself

Abstract

The wide use of cooperative missions using multiple unmanned platforms has made relative distance information an essential factor for cooperative positioning and formation control. Reducing the range error effectively in real time has become the main technical challenge. We present a new method to deal with ranging errors based on the distance increment (DI). The DI calculated by dead reckoning is used to smooth the DI obtained by the cooperative positioning, and the smoothed DI is then used to detect and estimate the non-line-of-sight (NLOS) error as well as to smooth the observed values containing random noise in the filtering process. Simulation and experimental results show that the relative accuracy of NLOS estimation is 8.17%, with the maximum random error reduced by 40.27%. The algorithm weakens the influence of NLOS and random errors on the measurement distance, thus improving the relative distance precision and enhancing the stability and reliability of cooperative positioning.

Indexed as

cooperative positioningdistance incrementdistance smoothingnon-line-of-sight error

Identifiers

PMID34884032
PMCPMC8659529

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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.