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

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 Reference stations Ionosphere

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

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

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

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 NetRTCM [mm] VRS [mm] Improv[%] Mean [mm] North -4.66 -3.30 East -4.29 -5.11 Height -117.00 -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

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 (1Hz) 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 L1/L2 measurements Helps to improve Positioning accuracy Ambiguity fixing

Positioning Error Comparison - East Error

Positioning Error Comparison – Height Error

Positioning Performance average 3D-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 FP

GPS/Glonass Network GPS Only GPS/GLN

Partial GPS/GLONASS Network GPS Only GPS/GLN FP

Sparse Glonass Network GPS Only GPS/GLN FP

A Dense GPS/GLONASS Test Network GPS only

A Sparse GPS/GLONASS Test Network GPS only

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

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

VRSNow Germany (145)

VRSNow Germany (Subnetwork)

VRSNow Germany (145)

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

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

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)

Computation Time Comparison (4 Core Dell Precision 490)

CPU Load (VRSNow Germany)

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