Evidence map›Paper›PMID 33167525›Full record

ArticleSensors (Basel, Switzerland)2020

Cooperative Localization and Time Synchronization Based on M-VMP Method.

Zhongliang Deng, Shihao Tang, Buyun Jia, Hanhua Wang, Xiwen Deng, Xinyu Zheng

Abstract readLetter
In one paragraph

Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Indoor Positioning and Navigation.Sensors (Basel, Switzerland) · 2021
    Article
  2. Article
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

6 authors.

Zhongliang DengSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Shihao TangSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Buyun JiaSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.ORCID 0000-0002-8619-6037
Hanhua WangSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.ORCID 0000-0002-8930-1082
Xiwen DengSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Xinyu ZhengSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.ORCID 0000-0003-1840-214X

Funding

National Key Research and Development Program of China 2016YFB0502003
6 · The paper itself

Abstract

Localization estimation and clock synchronization are important research directions in the application of wireless sensor networks. Aiming at the problems of low positioning accuracy and slow convergence speed in localization estimation methods based on message passing, this paper proposes a low-complexity distributed cooperative joint estimation method suitable for dynamic networks called multi-Gaussian variational message passing (M-VMP). The proposed method constrains the message to be a multi-Gaussian function superposition form to reduce the information loss in the variational message passing algorithm (VMP). Only the mean, covariance and weight of each message need to be transmitted in the network, which reduces the computational complexity while ensuring the information completeness. The simulation results show that the proposed method is superior to the VMP algorithm in terms of position accuracy and convergence speed and is close to the sum-product algorithm over a wireless network (SPAWN) based on non-parametric belief propagation, but the computational complexity and communication load are significantly reduced.

Indexed as

cooperative localizationfactor graph (FG)joint estimation of position and clockmulti-variational message passing (M-VMP)second-order Taylor expansion

Identifiers

PMID33167525
PMCPMC7663923

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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.