Evidence map›Paper›PMID 30400462›Full record

ArticleMicromachines2017

Modeling and Quantitative Analysis of GNSS/INS Deep Integration Tracking Loops in High Dynamics.

Yalong Ban, Xiaoji Niu, Tisheng Zhang, Quan Zhang, Jingnan Liu

Abstract read
In one paragraph

Article in Micromachines, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

5 authors.

Yalong BanGNSS Research Center, Wuhan University, 129 Luoyu Road, Wuhan 430079, China. ylban@whu.edu.cn.
Xiaoji NiuGNSS Research Center, Wuhan University, 129 Luoyu Road, Wuhan 430079, China. xjniu@whu.edu.cn.
Tisheng ZhangGNSS Research Center, Wuhan University, 129 Luoyu Road, Wuhan 430079, China. zts@whu.edu.cn.
Quan ZhangCollaborative Innovation Center of Geospatial Technology, 129 Luoyu Road, Wuhan 430079, China. zhangquan@whu.edu.cn.
Jingnan LiuGNSS Research Center, Wuhan University, 129 Luoyu Road, Wuhan 430079, China. jnliu@whu.edu.cn.

Funding

National High Technology Research and Develop Program of China 2015AA124002National Key R&D Program of China 2016YFB0501803National Natural Science Foundations of China 41404029
6 · The paper itself

Abstract

To meet the requirements of global navigation satellite systems (GNSS) precision applications in high dynamics, this paper describes a study on the carrier phase tracking technology of the GNSS/inertial navigation system (INS) deep integration system. The error propagation models of INS-aided carrier tracking loops are modeled in detail in high dynamics. Additionally, quantitative analysis of carrier phase tracking errors caused by INS error sources is carried out under the uniform high dynamic linear acceleration motion of 100 g. Results show that the major INS error sources, affecting the carrier phase tracking accuracy in high dynamics, include initial attitude errors, accelerometer scale factors, gyro noise and gyro g-sensitivity errors. The initial attitude errors are usually combined with the receiver acceleration to impact the tracking loop performance, which can easily cause the failure of carrier phase tracking. The main INS error factors vary with the vehicle motion direction and the relative position of the receiver and the satellites. The analysis results also indicate that the low-cost micro-electro mechanical system (MEMS) inertial measurement units (IMU) has the ability to maintain GNSS carrier phase tracking in high dynamics.

Indexed as

error propagation modelglobal navigation satellite systems (GNSS)/inertial navigation system (INS) deep integrationhigh dynamicsquantitative analysis

Identifiers

PMID30400462
PMCPMC6189995

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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.