Evidence map›Paper›PMID 29547552›Full record

ArticleSensors (Basel, Switzerland)2018

A Model of Gravity Vector Measurement Noise for Estimating Accelerometer Bias in Gravity Disturbance Compensation.

Junbo Tie, Juliang Cao, Lubing Chang, Shaokun Cai, Meiping Wu, Junxiang Lian

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 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Enhancing drainage pipeline inspection: advanced integration of six-axis sensor fusion and extended Kalman filter.Water science and technology : a journal of the International Association on Water Pollution Research · 2025
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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

6 authors.

Junbo TieCollege of Mechatronics and Automation, National University of Defense Technology, Changsha 410073, China. tiejunbo11@nudt.edu.cn.ORCID 0000-0002-8989-8931
Juliang CaoCollege of Mechatronics and Automation, National University of Defense Technology, Changsha 410073, China. cjvl@163.com.
Lubing ChangDepartment of Navigation Engineering, Naval University of Engineering, Wuhan 430000, China. changlubin@163.com.ORCID 0000-0002-6705-3401
Shaokun CaiCollege of Mechatronics and Automation, National University of Defense Technology, Changsha 410073, China. csk527@163.com.
Meiping WuCollege of Mechatronics and Automation, National University of Defense Technology, Changsha 410073, China. wu_meiping@163.com.
Junxiang LianCollege of Mechatronics and Automation, National University of Defense Technology, Changsha 410073, China. jx_lian@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Compensation of gravity disturbance can improve the precision of inertial navigation, but the effect of compensation will decrease due to the accelerometer bias, and estimation of the accelerometer bias is a crucial issue in gravity disturbance compensation. This paper first investigates the effect of accelerometer bias on gravity disturbance compensation, and the situation in which the accelerometer bias should be estimated is established. The accelerometer bias is estimated from the gravity vector measurement, and a model of measurement noise in gravity vector measurement is built. Based on this model, accelerometer bias is separated from the gravity vector measurement error by the method of least squares. Horizontal gravity disturbances are calculated through EGM2008 spherical harmonic model to build the simulation scene, and the simulation results indicate that precise estimations of the accelerometer bias can be obtained with the proposed method.

Indexed as

accelerometer biasestimationgravity disturbance compensationinertial navigation

Identifiers

PMID29547552
PMCPMC5876899

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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.