VRS Network The Magic Behind the Scene Xiaoming

VRS Network The Magic Behind the Scene Xiaoming Chen Trimble Terrasat Gmb. H

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

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

GNSS Positioning Error Sources

GNSS Network Models Ionosphere Reference stations

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 realtime – VRS – FKP – RTCM Network Message § Rover use the network corrected data to achieve better performance over longer distance

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

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, CMRx. Low bandwidth required § Support GLONASS

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

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

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

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

Benefits with VRS Net. RTCM [mm] Mean [mm] North -4. 66 -3. 30 East -4. 29 -5. 11 Height -117. 00 Standar Height 46. 12 d 2 D 30. 83 Deviatio n 3 D 55. 47 [mm] RMS [mm] VRS [mm] Improv[% ] -41. 34 39. 19 15. 1 26. 67 13. 9 47. 41 14. 5 Height 125. 76 56. 96 54. 7 2 D 31. 47 27. 36 13. 1 3 D 129. 64 63. 19 51. 3

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

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

Predicted Network Correction Quality (strong ionosphere)

Predicted Network Correction Quality (calm ionosphere)

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

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

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

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

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

Improving Rover Performance With Network Correction Quality § Predicted error statistics can help to improve positioning by – Better measurement weighting – Optimum combination of L 1/L 2 measurements § Helps to improve – Positioning accuracy – Ambiguity fixing

Positioning Error Comparison - East Error

Positioning Error Comparison – Height Error

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

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

GPS Network GPS Only GPS/GLN

GPS/Glonass Network GPS Only GPS/GLN

Partial GPS/GLONASS Network GPS Only GPS/GLN

Sparse Glonass Network GPS Only GPS/GLN

A Dense GPS/GLONASS Test Network GPS&GLONASS GPS only

A Sparse GPS/GLONASS Test Network GPS&GLONASS GPS only

Sparse GLONASS Test Results § Rover Initialization 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 RMS North [mm] RMS East [mm] RMS Height [mm] GPS Only 12 7 25 Dense GPS/GLN 12 6 23 Sparse GPS. GLN 12 6 23 § Rover Positioning Network Type

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 (1 Hz) § More Satellites – GPS – GLONASS – GALILEO § More Signals – L 5 – E 5 A, E 5 B

VRSNow Germany (145)

VRSNow Germany (Subnetwork)

VRSNow Germany (145)

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

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

Centralized Geometry filter Number of States

Centralized Geometry Filter Number of multiplications

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

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

Computation Time Comparison (4 Core Dell Precision 490)

CPU Load (VRSNow Germany)

Summary § Quality measures for RTK network corrections significantly improve the rover performance ü Positioning improved by up to a factor of 2 ü 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 ü Reduce the complexity of network administration