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Robust Kalman Filter Aided GEO/IGSO/GPS Raw-PPP/INS Tight Integration

Reliable and continuous navigation solutions are essential for high-accuracy location-based services. Currently, the real-time kinematic (RTK) based Global Positioning System (GPS) is widely utilized to satisfy such requirements. However, RTK’s accuracy and continuity are limited by the insufficient...

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Autores principales: Gao, Zhouzheng, Li, You, Zhuang, Yuan, Yang, Honglei, Pan, Yuanjin, Zhang, Hongping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6359615/
https://www.ncbi.nlm.nih.gov/pubmed/30669595
http://dx.doi.org/10.3390/s19020417
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author Gao, Zhouzheng
Li, You
Zhuang, Yuan
Yang, Honglei
Pan, Yuanjin
Zhang, Hongping
author_facet Gao, Zhouzheng
Li, You
Zhuang, Yuan
Yang, Honglei
Pan, Yuanjin
Zhang, Hongping
author_sort Gao, Zhouzheng
collection PubMed
description Reliable and continuous navigation solutions are essential for high-accuracy location-based services. Currently, the real-time kinematic (RTK) based Global Positioning System (GPS) is widely utilized to satisfy such requirements. However, RTK’s accuracy and continuity are limited by the insufficient number of the visible satellites and the increasing length of base-lines between reference-stations and rovers. Recently, benefiting from the development of precise point positioning (PPP) and BeiDou satellite navigation systems (BDS), the issues existing in GPS RTK can be mitigated by using GPS and BDS together. However, the visible satellite number of GPS + BDS may decrease in dynamic environments. Therefore, the inertial navigation system (INS) is adopted to bridge GPS + BDS PPP solutions during signal outage periods. Meanwhile, because the quality of BDS geosynchronous Earth orbit (GEO) satellites is much lower than that of inclined geo-synchronous orbit (IGSO) satellites, the predicted observation residual based robust extended Kalman filter (R-EKF) is adopted to adjust the weight of GEO and IGSO data. In this paper, the mathematical model of the R-EKF aided GEO/IGSO/GPS PPP/INS tight integration, which uses the raw observations of GPS + BDS, is presented. Then, the influences of GEO, IGSO, INS, and R-EKF on PPP are evaluated by processing land-borne vehicle data. Results indicate that (1) both GEO and IGSO can provide accuracy improvement on GPS PPP; however, the contribution of IGSO is much more visible than that of GEO; (2) PPP’s accuracy and stability can be further improved by using INS; (3) the R-EKF is helpful to adjust the weight of GEO and IGSO in the GEO/IGSO/GPS PPP/INS tight integration and provide significantly higher positioning accuracy.
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spelling pubmed-63596152019-02-06 Robust Kalman Filter Aided GEO/IGSO/GPS Raw-PPP/INS Tight Integration Gao, Zhouzheng Li, You Zhuang, Yuan Yang, Honglei Pan, Yuanjin Zhang, Hongping Sensors (Basel) Article Reliable and continuous navigation solutions are essential for high-accuracy location-based services. Currently, the real-time kinematic (RTK) based Global Positioning System (GPS) is widely utilized to satisfy such requirements. However, RTK’s accuracy and continuity are limited by the insufficient number of the visible satellites and the increasing length of base-lines between reference-stations and rovers. Recently, benefiting from the development of precise point positioning (PPP) and BeiDou satellite navigation systems (BDS), the issues existing in GPS RTK can be mitigated by using GPS and BDS together. However, the visible satellite number of GPS + BDS may decrease in dynamic environments. Therefore, the inertial navigation system (INS) is adopted to bridge GPS + BDS PPP solutions during signal outage periods. Meanwhile, because the quality of BDS geosynchronous Earth orbit (GEO) satellites is much lower than that of inclined geo-synchronous orbit (IGSO) satellites, the predicted observation residual based robust extended Kalman filter (R-EKF) is adopted to adjust the weight of GEO and IGSO data. In this paper, the mathematical model of the R-EKF aided GEO/IGSO/GPS PPP/INS tight integration, which uses the raw observations of GPS + BDS, is presented. Then, the influences of GEO, IGSO, INS, and R-EKF on PPP are evaluated by processing land-borne vehicle data. Results indicate that (1) both GEO and IGSO can provide accuracy improvement on GPS PPP; however, the contribution of IGSO is much more visible than that of GEO; (2) PPP’s accuracy and stability can be further improved by using INS; (3) the R-EKF is helpful to adjust the weight of GEO and IGSO in the GEO/IGSO/GPS PPP/INS tight integration and provide significantly higher positioning accuracy. MDPI 2019-01-21 /pmc/articles/PMC6359615/ /pubmed/30669595 http://dx.doi.org/10.3390/s19020417 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Gao, Zhouzheng
Li, You
Zhuang, Yuan
Yang, Honglei
Pan, Yuanjin
Zhang, Hongping
Robust Kalman Filter Aided GEO/IGSO/GPS Raw-PPP/INS Tight Integration
title Robust Kalman Filter Aided GEO/IGSO/GPS Raw-PPP/INS Tight Integration
title_full Robust Kalman Filter Aided GEO/IGSO/GPS Raw-PPP/INS Tight Integration
title_fullStr Robust Kalman Filter Aided GEO/IGSO/GPS Raw-PPP/INS Tight Integration
title_full_unstemmed Robust Kalman Filter Aided GEO/IGSO/GPS Raw-PPP/INS Tight Integration
title_short Robust Kalman Filter Aided GEO/IGSO/GPS Raw-PPP/INS Tight Integration
title_sort robust kalman filter aided geo/igso/gps raw-ppp/ins tight integration
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6359615/
https://www.ncbi.nlm.nih.gov/pubmed/30669595
http://dx.doi.org/10.3390/s19020417
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