1. VRS Network The Magic Behind the Scene
Xiaoming Chen
Trimble Terrasat GmbH

2. Outline GNSS Positioning Error Sources
General Introduction of Network RTK
VRS
RTCM 3 Network Message
Use Network Correction Quality To Improve Rover Performance
Sparse Glonass Network
Large Network Data Processing
Summary

3. GNSS Positioning Error Sources
Orbir Error
Satellite Clock Error
Îonosphere
Troposphere
Receiver Clock Error
Multipath

4. GNSS Positioning Error Sources

5. GNSS Network Models
Reference stations
Ionosphere

6. Network RTK
Utilize a reference station network to model distance dependent errors in real-time
Generate network corrected reference station data/corrections and transmit to rover in real-time
VRS
FKP
RTCM Network Message
Rover use the network corrected data to achieve better performance over longer distance

7. Network Processing Diagram
Raw Data
Analysis
Raw Data
Analysis
Raw Data
Analysis
Raw Data
Analysis
Synchronizer
Code-Carrier
Filters
Ionospheric
Filters
Geometric
Filter
Geometric
Filter
Geometric
Filter
Geometric
Filter
Geometric
Filter
Ambiguity Search & Fix
Network Model
Integrity
Residual Management
VRS/Net RTCM/FKP
Generation

8. Virtual Reference Station (VRS)
Computes tropospheric, orbit and ionospheric models in real time.
Derives an optimized VRS correction stream derived from these models for each rover
Requires bi-directional communication, also works with rebroadcast/RTCM VRS module
Based on RTCM, CMR, CMRx. Low bandwidth required
Support GLONASS

9. RTCM Network Message RTCM 3.1 standard Broadcast solution
Derive carrier ambiguities in network and generate observations on one ambiguity level (no ambiguities in the Double Difference sense)
Master & Auxiliary station
One master station
Up to 31 auxiliary stations (ambiguity “free” observations)
High bandwidth or lower rate for corrections
Network corrections are computed on the rover from a subset of the network
GPS only

10. Modeling Error Sources Server Centric vs. Rover Centric
VRS = Server Centric Approach: Complex error models are used:
Ionospheric model
Tropospheric model
RTCM Network Message = Rover Centric Approach:
Interpolation in the rover

11. Modeling Error Sources: An Example for tropospheric modeling
Station
Height [m]
Jungfraujoch
3634
Hohtenn
985
Sannen
1419
Zimmerwald
956
Huttil
779
Luzern
542
Andermatt
2367
Jungfraujoch
AGNES Network, Switzerland on July 7, 2003,
operated by Swisstopo with Trimble VRS
Jungfraujoch as rover
Nearest ref. station: Hohtenn

12. Modeling errors: VRS vs. RTCM Network Message
Iono-free Residuals for SV 05

13. Benefits with VRS NetRTCM [mm] VRS [mm] Improv[%] Mean [mm] North
-4.66
-3.30
East
-4.29
-5.11
Height
-41.34
Standard
Deviation
46.12
39.19
15.1 2D
30.83
26.67
13.9 3D
55.47
47.41
14.5
RMS
125.76
56.96
54.7
31.47
27.36
13.1
129.64
63.19
51.3

14. Use Correction Quality to Improve Rover Performance
Network RTK correction considered as interpolated corrections between reference stations
Interpolation is not perfect depending on actual atmosphere conditions
RTK Network server process provides quality estimates for residual interpolation
Can be used by the RTK rover to optimize RTK performance
Sparse GLONASS networks with reduced GLONASS correction quality

15. Residual Error Description
RTK Network generates a description of the dispersive and non-dispersive error for each satellite
Consists of constant, distance and height dependent terms

16. Predicted Network Correction Quality (strong ionosphere)

17. Predicted Network Correction Quality (calm ionosphere)

18. Network used for Evaluation of Quality Information
24 h data (1Hz)
5 Stations
1 Rover (33 km from 0272)

19. Ionospheric Residuals PRN 22
55% of the DD residuals < predicted sigmas

20. Geometric Residuals PRN 22
47% of the DD residuals < predicted sigmas

21. Ionospheric Residuals PRN 1
62% of the DD residuals < predicted sigmas

22. Ionospheric Residuals PRN 31
56% of the DD residuals < predicted sigmas

23. Improving Rover Performance With Network Correction Quality
Predicted error statistics can help to improve positioning by
Better measurement weighting
Optimum combination of L1/L2 measurements
Helps to improve
Positioning accuracy
Ambiguity fixing

24. Positioning Error Comparison - East Error

25. Positioning Error Comparison – Height Error

26. Positioning Performance
average 3D-RMS (½ hour slots)

27. Sparse GLONASS Network
Increasing number of RTK network service providers introduce GLONASS only on selected stations
RTK Servers have to handle sparse GLONASS coverage in dense GPS networks
Provide high quality GPS correction and acceptable GLONASS correction
RTK Rover performance is better or equal to GPS only solution

28. GPS Network
GPS Only
GPS/GLN FP

29. GPS/Glonass Network
GPS Only
GPS/GLN

30. Partial GPS/GLONASS Network
GPS Only
GPS/GLN FP

31. Sparse Glonass Network
GPS Only
GPS/GLN FP

32. A Dense GPS/GLONASS Test Network
GPS only

33. A Sparse GPS/GLONASS Test Network
GPS only

34. Sparse GLONASS Test Results
Rover Initialization
Rover Positioning
Network Type
68%[sec]
95%[sec]
No. Init.
GPS Only 14 18
2643
Dense GPS/GLN 12 15
2645
Sparse GPS/GLN 13 16
2644
Network Type
RMS North
[mm]
RMS East
RMS Height
GPS Only 12 7 25
Dense GPS/GLN 6 23
Sparse GPS.GLN

35. Large GNSS Network Data Processing
Increasing Complexity and Demand…
More Stations
Tendency to increase networks to more than 100 stations
Challenge to process all data on one server in real-time (1Hz)
More Satellites
GPS
GLONASS
GALILEO
More Signals L5
E5A, E5B

36. VRSNow Germany (145)

37. VRSNow Germany (Subnetwork)

38. VRSNow Germany (145)

39. Network Processing Diagram
Raw Data
Analysis
Raw Data
Analysis
Raw Data
Analysis
Raw Data
Analysis
Synchronizer
Code-Carrier
Filters
Ionospheric
Filters
Geometric
Filter
Ambiguity Search & Fix
Network Model
Integrity
Residual Management
VRS/Net RTCM/FKP
Generation 39

40. Centralized Geometry Filter
Provide iono.-free ambiguity for network ambiguity fixing
Provide ZTD estimation
All states estimated in a big (centralized) filter
Typical setup
ZTD per station
Receiver clock error per station
Satellite clock error per satellite
Ambiguity per station per satellite
Orbit error

41. Centralized Geometry filter Number of States

42. Centralized Geometry Filter Number of multiplications

43. Principle of Federated Filter
A bank of local Kalman filters runs parallel.
A central fusion processor computes an optimal weighted least-square estimate of the common system states and their covariance
Then the result of the central fusion processor is fed back to each local filter

44. Parallel Computing
Simultaneuous use of multiple compute resources to solve a computational problem

45. Computation Time Comparison (4 Core Dell Precision 490)

46. Computation Time Comparison (4 Core Dell Precision 490)

47. CPU Load (VRSNow Germany)

48. Summary
Quality measures for RTK network corrections significantly improve the rover performance
Positioning improved by up to a factor of 2
Initialization time reduced by 30%
Sparse GLONASS network provides decent rover performance during low to medium iono activity
Large network processing provide seamless and homogeneous solution cross the whole network with balanced CPU load
Reduce the complexity of network administration