Research on the Error of Global Positioning System Based on Time Series Analysis.

Lijun Song, Lei Zhou, Peiyu Xu, Wanliang Zhao, Shaoliang Li, Zhe Li

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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1 · What the graph read from it

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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.

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5 · Who and what money

Authors and funding

6 authors.

Lijun SongSchool of Information and Control Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China.ORCID 0000-0002-5169-962X

Lei ZhouSchool of Information and Control Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China.ORCID 0000-0003-4353-3088

Peiyu XuSchool of Information and Control Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China.

Wanliang ZhaoShanghai Aerospace Control Technology Institute, Shanghai 201100, China.

Shaoliang LiShanghai Aerospace Control Technology Institute, Shanghai 201100, China.

Zhe LiSchool of Information and Control Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China.

Funding

Natural Science Foundation of Shaanxi Province 2020JM-488special scientific research project of the Education Department of Shaanxi Province 20JK0728

6 · The paper itself

Abstract

Due to the poor dynamic positioning precision of the Global Positioning System (GPS), Time Series Analysis (TSA) and Kalman filter technology are used to construct the positioning error of GPS. According to the statistical characteristics of the autocorrelation function and partial autocorrelation function of sample data, the Autoregressive (AR) model which is based on a Kalman filter is determined, and the error model of GPS is combined with a Kalman filter to eliminate the random error in GPS dynamic positioning data. The least square method is used for model parameter estimation and adaptability tests, and the experimental results show that the absolute value of the maximum error of longitude and latitude, the mean square error of longitude and latitude and average absolute error of longitude and latitude are all reduced, and the dynamic positioning precision after correction has been significantly improved.

Indexed as

Geographic Information SystemsResearch DesignTime FactorsAutoregressive (AR) modelGlobal Positioning System (GPS)Kalman filteringpositioning precisionTime Series Analysis (TAS)

Identifiers

PMID35632023

DOI10.3390/s22103614

PMCPMC9145276

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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.