Advancing Precise Point Positioning With All-Source CSSR Message Augmentation

Real-time estimation of a precise ephemeris poses a formidable challenge for precise point positioning (PPP); such estimation potentially leads to degraded positioning accuracy and a long convergence time. In particular, the satellite-specific errors resulting from satellite positions, clock biases and code biases, can cause positioning errors on the order of meters, with the convergence time in the range of hours. To address this problem, next-generation services are provided by the Quasi-Zenith Satellite System, Galileo system and BeiDou system. These services broadcast satellite augmentation data through modernized signals in the compact state space representation (CSSR) format for use in realtime PPP. However, the availability of a single service is limited because it is not adaptable to different environments or regions. In this study, we proposed an advancing PPP by using all sources of satellite-based correction data. This method allows the number of usable satellites to reach 25 in a dynamic scenario because it maximizes the number of valid CSSR corrections. The proposed advanced CSSR-PPP addresses the problem of limited service availability, thus expanding applicability of real-time PPP with satellite-based augmentation data. Furthermore, a positioning engine was developed and validated through kinematic experiments. This engine achieved a horizontal root mean square error of 0.19 m and a 95th-percentile horizontal error of 0.28 m by using a low-cost global navigation satellite system receiver in a suburban region. The proposed CSSR-PPP method is competitive with the postprocessing-based Canadian Spatial Reference System PPP service.

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Publication Details

Journal
IEEE Sensors Journal
Published
2025-04-16
DOI
https://doi.org/10.1109/jsen.2025.3557439
Citations
1
Primary Topic
Inertial Sensor and Navigation
Type
article
Field-Weighted Citation Impact
4.99

Funders

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article

Advancing Precise Point Positioning With All-Source CSSR Message Augmentation

Cheng-Wei Wang, Shau‐Shiun Jan
1 citations
IEEE Sensors Journal
Inertial Sensor and Navigation
4.99
article

Advancing Precise Point Positioning With All-Source CSSR Message Augmentation

Cheng-Wei Wang, Shau‐Shiun Jan
article en
1 citations

Abstract

Real-time estimation of a precise ephemeris poses a formidable challenge for precise point positioning (PPP); such estimation potentially leads to degraded positioning accuracy and a long convergence time. In particular, the satellite-specific errors resulting from satellite positions, clock biases and code biases, can cause positioning errors on the order of meters, with the convergence time in the range of hours. To address this problem, next-generation services are provided by the Quasi-Zenith Satellite System, Galileo system and BeiDou system. These services broadcast satellite augmentation data through modernized signals in the compact state space representation (CSSR) format for use in realtime PPP. However, the availability of a single service is limited because it is not adaptable to different environments or regions. In this study, we proposed an advancing PPP by using all sources of satellite-based correction data. This method allows the number of usable satellites to reach 25 in a dynamic scenario because it maximizes the number of valid CSSR corrections. The proposed advanced CSSR-PPP addresses the problem of limited service availability, thus expanding applicability of real-time PPP with satellite-based augmentation data. Furthermore, a positioning engine was developed and validated through kinematic experiments. This engine achieved a horizontal root mean square error of 0.19 m and a 95th-percentile horizontal error of 0.28 m by using a low-cost global navigation satellite system receiver in a suburban region. The proposed CSSR-PPP method is competitive with the postprocessing-based Canadian Spatial Reference System PPP service.

IEEE Sensors JournalVol. 25(11)
National Cheng Kung University (TW)
National Science and Technology Council
Openalex Percentile: Top 7%
Inertial Sensor and Navigation
4.99
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