410 likes | 517 Views
Part V Centimeter-Level Instantaneous Long-Range RTK: Methodology, Algorithms and Application. GS894G. Presentation Outline. Network RTK – Concept and Benefits Research Objectives The MPGPS™ Software Methodology and Algorithms Experiments and Test Results Summary and Conclusions
E N D
Part V Centimeter-Level Instantaneous Long-Range RTK: Methodology, Algorithms and Application GS894G
Presentation Outline • Network RTK – Concept and Benefits • Research Objectives • The MPGPS™ Software • Methodology and Algorithms • Experiments and Test Results • Summary and Conclusions • Current and Future Developments
Network RTK – Concept and Benefits Traditional RTK limitations (single baseline) • Limited to short distances (~10 km) • Ionospheric and tropospheric refraction are the main error sources Network RTK • Atmospheric corrections are evaluated in the network and broadcast to the user receiver location • Single or multi-baseline instantaneous rover solution • Long distances – over 100 km • Centimeter-level accuracy • Suitable for geodetic, surveying and navigation applications • Takes advantage of already available network GPS infrastructure
Network RTK – Concept and Benefits Instantaneous RTK Advantages • Due to epoch independence, resistant to negative effects, such as:Cycle slips and loss of lock • No initialization required for short/medium baselines (~< 50 km) • Short initialization (a few epochs) for longer baselines (~> 50 km) • Provides cm-level accuracy Disadvantages • Challenging ambiguity resolution and validation for long baselines instantaneous = single-epoch
Research Objectives • Develop and evaluate state-of-the-art methodology and algorithms for cm-level long-range instantaneous RTK GPS • Analyze the infrastructure necessary to support long-range instantaneous RTK GPS • Investigate atmospheric correction accuracy obtained from GPS reference network with station separation of 100-200 km supporting long-range RTK
MPGPS™ Multi Purpose GPS Processing Software • Developed at Ohio State University (OSU) • Positioning Modules • Long-range instantaneous RTK GPS • Rapid-static • Static • Multi-station DGPS • Precise point positioning (PPP) • Atmospheric Modules • Ionosphere modeling and mapping • Troposphere modeling • Positioning Solutions • Single-baseline • Multi-baseline (network) • Stand-alone
Methodology - Functional Model (DD) • satellite indexes • station indexes • DD phase and code observation on frequency n • DD geometric distance • - tropospheric total zenith delay (TZD) • - troposphere mapping function • - DD ionospheric delay • - GPS frequencies on L1 and L2 • - GPS frequency wavelengths on L1 and L2 • - carrier phase ambiguities
Methodology - Adjustment Model Sequential Generalized Least Squares (GLS) • All parameters in the mathematical model are considered pseudo-observations with a priori information (σ = 0 ÷ ) • Two characteristic groups of interest: - instantaneous parameters (e.g., ionospheric delays)- accumulated parameters (e.g., ambiguities) • Flexibility, easy implementation of: • stochastic constraints • fixed constraints • weighted parameters • Filters (e.g., forward, backward)
Methodology - Network Solution Network correction generation • Precisely known reference station coordinates • Double-difference (DD) ionospheric delay estimation and decomposition to zero-difference (ZD) • Single layer model (SLM) ionosphere approximation • Tropospheric total zenith delay (TZD) estimation Ambiguity resolution (AR) • Least square AMBiguity Decorrelation Algorithm (LAMBDA) Validation • W-ratio and success-rate Unknowns • DD Ionospheric delays, Tropospheric TZD per station, DD ambiguities The network corrections are broadcast to the rover in a form of a grid
Methodology - Network Solution Ionospheric delay decomposition • n-2 linearly independentDD observation equations for an individual baseline and n ZDs, thus rigorous decomposition is not possible Solutions • Introduce additional independent constraints on at least two ZD delay parameters • Introduce looseconstraints to the diagonal of the normal matrix • Both methods are numerically identical • However, the first method results in an “unbiased” estimate while the second one provides a “biased” estimate in the least squares sense
Methodology - Network Solution Single layer model (SLM) ionosphere approximation • Slant ionospheric delays estimation from dual-frequency GPS data at the permanent stations • Slant ionospheric delays conversion to vertical total electron content (VTEC) at ionosphere pierce points (IPPs) • Kriging interpolation to produce LIM in a form of a grid using the calculated vertical TEC values at IPPs
Methodology - Network Solution SLM – Single Layer Model z - zenith angle H - SLM height R - Earth radius 1 TECU = 1016 ellectron/m2 = 0.162 m delay/advance SLM assumes that all free electrons are contained in a shell of infinitesimal thickness at altitude H
Methodology - Rover Solution Single or multi-baseline mode • Step one – short on-the-fly (OTF) initialization (a few epochs) • Ionospheric and tropospheric delays are provided by the network to initiate the rover solution • Step two – instantaneous (single-epoch) • DD ionospheric delays from the previous correctly resolved epoch are applied in the rover solution as a prediction • TZD provided from the network • Unknowns • Rover position and ambiguities • The DD ionospheric delays and TZD are tightly constrained in the GLS adjustment
Experiments and Test Results 1. Ionospheric model comparison (Ohio CORS) Quality test of several ionosphere modeling techniques derived from GPS permanent tracking network data • Local – MPGPS-NR (network RTK) • Regional – NGS MCON and NGS MAGIC • Global ionospheric models – IGS global ionosphere map (GIM) 2. Instantaneous long-range RTK analysis (Ohio CORS) • Distances between reference stations ~200 km • Distances to the rover ~100 km 3. Network RTK in the state of Israel - GIL network (GPS in Israel) The impact of the ionospheric correction latency on long-baseline instantaneous kinematic GPS positioning
Experiments and Test Results (1) The ionospheric models • MPGPS™-NR — network RTK (NR) dual frequency carrier phase-based model, decomposed from DD ionospheric delays; single layer; local – uses several stations closest to the rover • IGS GIM — international GPS Service (IGS) global ionospheric map (GIM); single layer; global - ~200 stations • NGS ICON — absolute model based on undifferenced dual-frequency ambiguous carrier phase data; single layer; regional - ~340 CORS stations (USA) • NGS MAGIC — carrier phase DD-based tomographic method; 3D; regional - ~150 CORS and IGS stations (USA)
Experiments and Test Results (1) Test data • Ohio CORS, August 31, 2003 • 24-h data set was processed in 12 sessions, each 2-h long • 30-s data sampling rate • Predicted satellite orbits and clock corrections (IGS) • Different reference satellite for each session • Varying ionospheric total electron content (TEC) levels • Varying GPS constellation • KNTN CORS station was selected as rover for the simulation
Experiments and Test Results (1) 108km 98km KNTN LSBN 109km 124km KNTN (rover) 63km 104km Network map Baseline map Rover baseline solution Network solution atmospheric corrections Test area maps (Ohio CORS)
Experiments and Test Results (1) Estimated and interpolated DD ionospheric correctionsfor the analyzed models, 24 h, KNTN-SIDN (60 km)
Experiments and test Results (1) DD ionospheric residuals with respect to the reference “truth” MPGPS-L4 KNTN-SIDN (60 km), 24 h
Experiments and Test Results (1) Estimated and interpolated DD ionospheric correctionsfor the analyzed models, 24 h, KNTN-DFI (100 km)
Experiments and Test results (1) DD ionospheric residuals with respect to the reference “truth” MPGPS-L4 KNTN-DEFI (100 km), 24 h
Experiments and Test Results (1) Residual statistics (24h) Ionospheric delay residual statistics 5 and 10 cm cut-off limits ±5 cm = ~1/4 of a cycle (required for pure instantaneous) ±10 cm = ~1/2 of a cycle
Experiments and Test Results (1) Examples of instantaneous positioning after 3-epoch OTF initialization, MPGPS-NR n,e,u, residuals with respect to the known coordinates, KNTN-SIDN, 60 km n,e,u, residuals with respect to the known coordinates,KNTN-DEFI, 100 km
Experiments and Test Results (1) Summary and conclusions • Different ionospheric models were analyzed • Varying TEC levels, generally quiet ionosphericconditions • Varying GPS constellation • MPGPS-NR provided the best solution • Can support instantaneous AR and high-accuracy positioning • Ionospheric correction accuracy of 1-6 cm (1 sigma) • Stochastic constraints in the GLS depend on the ionospheric activity level • Other models: lower rate of success of instantaneous AR
Experiments and Test Results (2) Test data • Ohio CORS, August 31, 2003 • Four two-hour sessions (daytime): • 14 - 16 UT (10pm - 12pm LT) • 16 - 18 UT (12pm - 14pm LT) • 18 - 20 UT (14pm - 16pm LT) • 20 - 22 UT (16am - 18pm LT) • 30-second sampling rate (i.e., 120 epochs per session) • Predicted satellite orbits and clock corrections (IGS) • Distances between reference stations ~200 km • Distances to the rover >100 km • COLB CORS station was selected as rover for the simulation
Experiments and Test Results (2) Network map Baseline map LSBN 193 km 206 km (rover) 121 km 212 km Roverbaseline solution Network solution atmospheric corrections Test area maps (Ohio CORS)
Experiments and Test Results (2) 14-16 UT (10am-12pm LT) 16-18 UT (12pm-14pm LT) Residuals (n,e,u ) with respect to the known coordinates, COLB-LEBA, 121 km,and satellite visibility at station COLB
Experiments and Test Results (2) 20-22 UT (16pm-18-pm LT) 18-20 UT (14pm-16pm LT) Residuals (n,e,u ) with respect to the known coordinates, COLB-LEBA, 121 km,and satellite visibility at station COLB
Experiments and Test Results (2) Summary and conclusions • A sub-network characterized by inter-station separation of~200 km was chosen to generate the atmospheric corrections • The rover-reference stations distances are larger than 100 km • Although these large distances, centimeter-levelinstantaneous rover positioning was demonstrated in this simulation. • The vertical component is weaker than the horizontal ones, as expected • It may be concluded that 200 km separation between the GPS reference stations is a sufficient infrastructure for centimeter- level long range instantaneous RTK
Experiments and Test Results (3) Test data • GIL (GPS in Israel) permanent network, June 21, 2004 • Four one-hour sessions: • 01 - 02 UT ( 4am - 5am LT) - sunrise • 09 - 10 UT (12am - 13pm LT) - noon • 13 - 14 UT (16am - 17pm LT) - afternoon • 17 - 19 UT (20am - 21pm LT) - sunset • 10-second sampling rate (i.e., 360 epochs per session) • Predicted satellite orbits and clock corrections (IGS) • Distances between reference stations ~100-200 km • Distances to the rover ~50-100 km • GILB station was selected as rover for the simulation
Test area map (GIL network) Experiments and Test Results (3) (1083m) Distances Reference network: 110-180 km To the rover: 50, 85, 98 km Station heights 37-1083 m 112 km Rover h=507m 85 km (37m) 50 km 180 km 98 km 110 km The network provides atmospheric corrections to therover (GILB) (32m) The rover station does not contribute to the atmospheric corrections
MPGPS-derived TEC Maps for the four analyzed sessions 12:30 pm LT (noon) highest TEC lowest gradients 4:30 am LT (sunrise) lowest TEC highest gradients 16:30 pm LT (afternoon) 20:30 pm LT (sunset) Experiments and Test Results (3)
DD ionospheric delay residuals, interpolated vs. “true” (MPGPS-L4),GILB-DRAG baseline (98 km) 4-5 am LT sunrise 12-13 pm LTnoon 16-17 pm LT- afternoon 20-21 pm LTsunset Experiments and Test Results (3) 5 cm is the accuracy limit for pure instantaneous
DD ionospheric delay residuals (with respect to MPGPS-L4) for different latencies, GILB-CSAR baseline (50 km), 4–5 am LT Experiments and Test Results (3) The large residuals reflect the high gradients (sunrise)
Single-baseline RTK position residuals (n,e,u) with respect to the known coordinates, GILB-CSAR (50 km), 4–5 am LT Experiments and Test results (3) 10 s latency, single-baseline 90 s latency, single-baseline unresolved ambiguities
Multi-baseline RTK position residuals (n,e,u) with respect to the known coordinates,GILB-CSAR (50 km) and GILB-ELRO (85), 4–5 am LT 90 s latency, single-baseline 90 s latency, multi-baseline Experiments and Test Results (3)
DD ionospheric delay residuals with respect to MPGPS-L4 for different latencies, GILB-DRAG baseline (98 km), 12–13 am LT Experiments and Test Results (3) The small residuals reflect the low gradients (noon)
Single-baseline RTK position residuals (n,e,u) with respect to the known coordinates,GILB-DRAG (98 km), 12–13 am LT 90 s latency 10 s latency Experiments and Test Results (3)
Experiments and Test Results (3) Statistics: instantaneous AR success rate Example AR success (%), single-baseline solution Note: the multi-base solution solved 100% of the ambiguities
Experiments and Test Results (3) Summary and conclusions • The interpolated ionospheric correctionaccuracy may not always be sufficient to assure pure instantaneous AR in the baseline mode (more than 5 cm) • It was demonstrated that when the single-baseline solution fails and the multi-baseline solution takes over, instantaneous AR can be sustained • For the existing ionospheric conditions, network configuration and processed baseline lengths (~100 km), 90-second latency seems to be a limit for reliable instantaneous AR • Once the ambiguities are correctly resolved, centimeter-level positioning can be assured over long baselines (>100 km) • Again, the vertical component is weaker than the horizontal ones • The long-range instantaneous RTK module in the MPGPS™ software can provide cm-level rover position over long distances
Algorithm applications Current • OPUS-RS - Extending the NGS (National Geodetic Survey) OPUS (On-line Positioning User Service) with rapid static capability, based on the MPGPS™ Network RTK algorithms • Current requirement – 2-4 hours • Future rapid static requirement – 10-15 minutes • Predicted user numberincrease – about 10 times • ICON and MAGIC - Quality evaluation of the two NGS ionosphere models Future • Further analysis to fully asses the RTK algorithm capabilities • Longer latencies (up to a few minutes using multi-base solution) • Different ionospheric conditions (i.e., ionospheric storms) • Longer baselines • Real kinematic data