Evidence map›Paper›PMID 29757983›Full record

ArticleSensors (Basel, Switzerland)2018

A Combined Gravity Compensation Method for INS Using the Simplified Gravity Model and Gravity Database.

Xiao Zhou, Gongliu Yang, Jing Wang, Zeyang Wen

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 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. 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

4 authors.

Xiao ZhouSchool of Instrument Science and Opto-Electronics Engineering, Beihang University, Beijing 100191, China. by1317110@buaa.edu.cn.
Gongliu YangSchool of Instrument Science and Opto-Electronics Engineering, Beihang University, Beijing 100191, China. yanggongliu@buaa.edu.cn.
Jing WangSchool of Mechatronices and Information Engineering, China Mining University, Beijing 100083, China. by1217133@buaa.edu.cn.
Zeyang WenSchool of Instrument Science and Opto-Electronics Engineering, Beihang University, Beijing 100191, China. by1717133@buaa.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In recent decades, gravity compensation has become an important way to reduce the position error of an inertial navigation system (INS), especially for a high-precision INS, because of the extensive application of high precision inertial sensors (accelerometers and gyros). This paper first deducts the INS's solution error considering gravity disturbance and simulates the results. Meanwhile, this paper proposes a combined gravity compensation method using a simplified gravity model and gravity database. This new combined method consists of two steps all together. Step 1 subtracts the normal gravity using a simplified gravity model. Step 2 first obtains the gravity disturbance on the trajectory of the carrier with the help of ELM training based on the measured gravity data (provided by Institute of Geodesy and Geophysics; Chinese Academy of sciences), and then compensates it into the error equations of the INS, considering the gravity disturbance, to further improve the navigation accuracy. The effectiveness and feasibility of this new gravity compensation method for the INS are verified through vehicle tests in two different regions; one is in flat terrain with mild gravity variation and the other is in complex terrain with fierce gravity variation. During 2 h vehicle tests, the positioning accuracy of two tests can improve by 20% and 38% respectively, after the gravity is compensated by the proposed method.

Indexed as

error modellingextreme learning machine (ELM)gravity compensationgravity modelhigh precision free-INS

Identifiers

PMID29757983
PMCPMC5982654

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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.