GNSS Precise Point Positioning Based on Dynamic Kalman Filter with Attenuation Factor

Yufu Guo; Senchun Chai; Lingguo Cui

doi:10.23919/ChiCC.2018.8484219

GNSS Precise Point Positioning Based on Dynamic Kalman Filter with Attenuation Factor

Yufu Guo, Senchun Chai, Lingguo Cui

School of Automation

Beijing Institute of Technology

Research output: Contribution to journal › Conference article › peer-review

Abstract

This paper addresses the Kalman filter divergence problem for Global Navigation Satellite System (GNSS)in Precise Point Positioning (PPP). Continuous improvement of precision of GNSS PPP makes it widely used in various fields, however the Kalman filter divergence resolutions for PPP signal attenuating and satellite missing condition are still insufficient. Kalman filter is a recursive, linear unbiased, minimum variance method. Compared with the standard Kalman filter, the dynamic Kalman filter with attenuation factor in this paper makes up for the Kalman filter divergence problem. The effectiveness of dynamic Kalman Filter is demonstrated via matlab simulation.

Original language	English
Article number	8484219
Pages (from-to)	4734-4738
Number of pages	5
Journal	Chinese Control Conference, CCC
Volume	2018-January
DOIs	https://doi.org/10.23919/ChiCC.2018.8484219
Publication status	Published - 2018
Event	37th Chinese Control Conference, CCC 2018 - Wuhan, China Duration: 25 Jul 2018 → 27 Jul 2018

Keywords

Attenuation factor
Kalman divergence
Precise Point Positioning

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10.23919/ChiCC.2018.8484219

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@article{ce19014638c64e1f88cd3a58d7036567,

title = "GNSS Precise Point Positioning Based on Dynamic Kalman Filter with Attenuation Factor",

abstract = "This paper addresses the Kalman filter divergence problem for Global Navigation Satellite System (GNSS)in Precise Point Positioning (PPP). Continuous improvement of precision of GNSS PPP makes it widely used in various fields, however the Kalman filter divergence resolutions for PPP signal attenuating and satellite missing condition are still insufficient. Kalman filter is a recursive, linear unbiased, minimum variance method. Compared with the standard Kalman filter, the dynamic Kalman filter with attenuation factor in this paper makes up for the Kalman filter divergence problem. The effectiveness of dynamic Kalman Filter is demonstrated via matlab simulation.",

keywords = "Attenuation factor, Kalman divergence, Precise Point Positioning",

author = "Yufu Guo and Senchun Chai and Lingguo Cui",

note = "Publisher Copyright: {\textcopyright} 2018 Technical Committee on Control Theory, Chinese Association of Automation.; 37th Chinese Control Conference, CCC 2018 ; Conference date: 25-07-2018 Through 27-07-2018",

year = "2018",

doi = "10.23919/ChiCC.2018.8484219",

language = "English",

volume = "2018-January",

pages = "4734--4738",

journal = "Chinese Control Conference, CCC",

issn = "1934-1768",

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TY - JOUR

T1 - GNSS Precise Point Positioning Based on Dynamic Kalman Filter with Attenuation Factor

AU - Guo, Yufu

AU - Chai, Senchun

AU - Cui, Lingguo

PY - 2018

Y1 - 2018

N2 - This paper addresses the Kalman filter divergence problem for Global Navigation Satellite System (GNSS)in Precise Point Positioning (PPP). Continuous improvement of precision of GNSS PPP makes it widely used in various fields, however the Kalman filter divergence resolutions for PPP signal attenuating and satellite missing condition are still insufficient. Kalman filter is a recursive, linear unbiased, minimum variance method. Compared with the standard Kalman filter, the dynamic Kalman filter with attenuation factor in this paper makes up for the Kalman filter divergence problem. The effectiveness of dynamic Kalman Filter is demonstrated via matlab simulation.

AB - This paper addresses the Kalman filter divergence problem for Global Navigation Satellite System (GNSS)in Precise Point Positioning (PPP). Continuous improvement of precision of GNSS PPP makes it widely used in various fields, however the Kalman filter divergence resolutions for PPP signal attenuating and satellite missing condition are still insufficient. Kalman filter is a recursive, linear unbiased, minimum variance method. Compared with the standard Kalman filter, the dynamic Kalman filter with attenuation factor in this paper makes up for the Kalman filter divergence problem. The effectiveness of dynamic Kalman Filter is demonstrated via matlab simulation.

KW - Attenuation factor

KW - Kalman divergence

KW - Precise Point Positioning

UR - http://www.scopus.com/inward/record.url?scp=85062684713&partnerID=8YFLogxK

U2 - 10.23919/ChiCC.2018.8484219

DO - 10.23919/ChiCC.2018.8484219

M3 - Conference article

AN - SCOPUS:85062684713

SN - 1934-1768

VL - 2018-January

SP - 4734

EP - 4738

JO - Chinese Control Conference, CCC

JF - Chinese Control Conference, CCC

M1 - 8484219

T2 - 37th Chinese Control Conference, CCC 2018

Y2 - 25 July 2018 through 27 July 2018

ER -

GNSS Precise Point Positioning Based on Dynamic Kalman Filter with Attenuation Factor

Abstract

Keywords

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