Evidence map›Paper›PMID 39771655›Full record

ArticleSensors (Basel, Switzerland)2024

In-Motion Forward-Forward Backtracking Fine Alignment Based on Displacement Observation for SINS/GNSS.

Yongyun Zhu, Yaohui Zhu, Xinhua Wei, Bingbo Cui, Shede Liu

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. 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

5 authors.

Yongyun ZhuSchool of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China.ORCID 0000-0002-3059-9232
Yaohui ZhuSchool of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China.
Xinhua WeiSchool of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China.
Bingbo CuiSchool of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China.ORCID 0000-0001-7436-7663
Shede LiuSchool of Instrument Science & Engineering, Southeast University, Nanjing 210096, China.

Funding

Jiangsu Province and Education Ministry Co-Sponsored Synergistic Innovation Center of Modern Agricultural Equipment Grant XTCX2009Natural Science Foundation of China Grant 32271999Natural Science Foundation of China Grant 32401719Natural Science Foundation of Jiangsu Province Grant BK20240877Open Funding from the Key Laboratory of Modern Agricultural Equipment and Technology Grant MAET202301Priority Academic Program Development of Jiangsu Higher Education Institutions Grant PAPD-2018-87
6 · The paper itself

Abstract

To solve the problem of slow convergence seen in the traditional fine alignment algorithm based on linear Kalman filtering, a forward-forward backtracking fine alignment algorithm for SINS is proposed after reanalyzing the fine alignment model in this paper. First, the forward-forward backtracking fine alignment model in initial navigation frame was derived. The displacement vector of the carrier in the initial navigation frame solved by GNSS positioning was utilized as the observation of the fine alignment model. Second, under the premise of storing only part of the navigation data, the initial alignment convergence speed was improved by backtracking and reusing the navigation data. The experimental results of the simulation and vehicle tests showed that each backtracking alignment can improve the accuracy of the fine alignment to the performance requirements of the initial alignment, which proved the effectiveness and feasibility of the backtracking fine alignment algorithm proposed in this paper.

Indexed as

displacement observationfine alignmentforward–forward backtrackingSINS/GNSS

Identifiers

PMID39771655
PMCPMC11679397

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Registered trials

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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.