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Using PPP Information to Implement a Global Real-Time Virtual Network DGNSS Approach

Wang Hu, Ashim Neupane, Jay A. Farrell

TL;DR

This work tackles the challenge of delivering centimeter‑ to meter‑level GNSS corrections on a global scale without deploying dense networks of physical reference stations. It proposes VN‑DGNSS, a PPP‑based, SSR‑driven server–client system that constructs RTCM OSR corrections at user locations by solving for satellite position and transmission time and by deriving common‑mode corrections from public SSR data. Real‑time, single‑frequency, pseudorange tests using public SSR sources demonstrate corrections that surpass SAE specifications and approach lane‑level accuracy, with open‑source implementation enabling broad accessibility. The approach lays the groundwork for future extensions to multi‑frequency, carrier‑phase positioning, GLONASS, and PPP‑AR techniques, potentially transforming wide‑area, high‑precision GNSS services.

Abstract

Differential GNSS (DGNSS) has been demonstrated to provide reliable, high-quality range correction information enabling real-time navigation with centimeter to sub-meter accuracy, which is required for applications such as connected and autonomous vehicles. However, DGNSS requires a local reference station near each user. For a continental or global scale implementation, this information dissemination approach would require a dense network of reference stations whose construction and maintenance would be prohibitively expensive. Precise Point Positioning affords more flexibility as a public service for GNSS receivers, but its State Space Representation format is not supported by most receivers in the field or on the market. This article proposes a novel Virtual Network DGNSS (VN-DGNSS) approach and an optimization algorithm that is key to its implementation. The approach capitalizes on the existing PPP infrastructure without the need for new physical reference stations. By connecting to public GNSS SSR data services, a VN-DGNSS server maintains current information about common-mode errors. Construction of the RTCM Observation Space Representation messages from this SSR information requires both the signal time-of-transmission and the satellite position at that time which are consistent with the time-of-reception for each client. This article presents an algorithm to determine these quantities. The results of real-time stationary and moving platform evaluations are included, using u-blox M8P and ZED-F9P receivers. The performance surpasses the SAE specification (68% of horizontal error <= 1.5 m and vertical error <= 3 m) and shows significantly better horizontal performance than GNSS Open Service. The moving tests also show better horizontal performance than the ZED-F9P receiver with SBAS enabled and achieve the lane-level accuracy (95% of horizontal errors less than 1 meter).

Using PPP Information to Implement a Global Real-Time Virtual Network DGNSS Approach

TL;DR

This work tackles the challenge of delivering centimeter‑ to meter‑level GNSS corrections on a global scale without deploying dense networks of physical reference stations. It proposes VN‑DGNSS, a PPP‑based, SSR‑driven server–client system that constructs RTCM OSR corrections at user locations by solving for satellite position and transmission time and by deriving common‑mode corrections from public SSR data. Real‑time, single‑frequency, pseudorange tests using public SSR sources demonstrate corrections that surpass SAE specifications and approach lane‑level accuracy, with open‑source implementation enabling broad accessibility. The approach lays the groundwork for future extensions to multi‑frequency, carrier‑phase positioning, GLONASS, and PPP‑AR techniques, potentially transforming wide‑area, high‑precision GNSS services.

Abstract

Differential GNSS (DGNSS) has been demonstrated to provide reliable, high-quality range correction information enabling real-time navigation with centimeter to sub-meter accuracy, which is required for applications such as connected and autonomous vehicles. However, DGNSS requires a local reference station near each user. For a continental or global scale implementation, this information dissemination approach would require a dense network of reference stations whose construction and maintenance would be prohibitively expensive. Precise Point Positioning affords more flexibility as a public service for GNSS receivers, but its State Space Representation format is not supported by most receivers in the field or on the market. This article proposes a novel Virtual Network DGNSS (VN-DGNSS) approach and an optimization algorithm that is key to its implementation. The approach capitalizes on the existing PPP infrastructure without the need for new physical reference stations. By connecting to public GNSS SSR data services, a VN-DGNSS server maintains current information about common-mode errors. Construction of the RTCM Observation Space Representation messages from this SSR information requires both the signal time-of-transmission and the satellite position at that time which are consistent with the time-of-reception for each client. This article presents an algorithm to determine these quantities. The results of real-time stationary and moving platform evaluations are included, using u-blox M8P and ZED-F9P receivers. The performance surpasses the SAE specification (68% of horizontal error <= 1.5 m and vertical error <= 3 m) and shows significantly better horizontal performance than GNSS Open Service. The moving tests also show better horizontal performance than the ZED-F9P receiver with SBAS enabled and achieve the lane-level accuracy (95% of horizontal errors less than 1 meter).

Paper Structure

This paper contains 24 sections, 30 equations, 4 figures, 8 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. GNSS Pseudorange Observations
  3. Satellite Position, Range, and Time-of-Transmission
  4. Algorithm Definition
  5. Effect of Common Mode Errors
  6. Common-mode Error Calculations
  7. Satellite Orbit and Clock Corrections
  8. Satellite Hardware Bias
  9. Global Troposphere Model
  10. Ionosphere Model
  11. Discussion
  12. VN-DGNSS Server-Client System design
  13. System Architecture
  14. Implementation Strategies
  15. Experimental Performance Analysis
  16. ...and 9 more sections

Figures (4)

  • Figure 1: VN-DGNSS server-client architecture
  • Figure 2: Stationary experimental results. Subplots (a), (b), and (c) show the cumulative error distributions for the horizontal, vertical, and 3D position error metrics: blue for SF GPS SPS, orange for DF GNSS OS, green for SF GPS VN, purple for SF GNSS VN, and yellow for F9P SBAS.
  • Figure 3: Experimental results for a moving platform test using a single-band antenna. Subplots (a), (c), and (e) show the horizontal, vertical, and 3D position error plotted versus time. The $x$-axis is the epoch number. Subplots (b), (d), and (f) show the cumulative error distribution of the horizontal, vertical, and 3D position error metrics: blue for SF GNSS VN, red for SF GNSS OS, and green for F9P SBAS.
  • Figure 4: Experimental results for a moving platform test using a dual-band antenna. Subplots (a), (c), and (e) show the horizontal, vertical, and 3D position error plotted versus time. The $x$-axis is the epoch number. Subplots (b), (d), and (f) show the cumulative error distribution of the horizontal, vertical, and 3D metrics: blue for SF GNSS VN; red for DF GNSS OS; and green for F9P SBAS.