The Ohio State University IGS LEO/GPS Pilot Project: Status and Future Plans

Transcription

1 The Ohio State University IGS LEO/GPS Pilot Project: Status and Future Plans Dorota Brzezinska, Jay Kwon and C. K. Shum Department of Civil and Environmental Engineering and Geodetic Science OSU The Ohio State University ION GPS 1 Salt Lake City, September 11-1, 1

2 The authors would like to acknowledge the contribution of the graduate students from the Department of Civil and Environmental Engineering and Geodetic Science,OSU Chang-Ki Hong and Tea-Suk Bae

3 Outline OSU IGS/LEO Associate Analysis Center (AAC) Mission and operational aspects POD techniques supported Kinematic POD for CHAMP: test results Summary and Conclusions

4 OSU IGS/LEO Associate Analysis Center Established in Major objectives: Near real- time POD for operational use in radio occultation technique of excess path delay estimation Post-processed LEO POD Atmospheric profiling for GPS Meteorology Establishing a network of permanent GPS stations Triple difference dynamic GPS POD (OSU GODIVA; Grejner-Brzezinska 199; Yang, 199; Goad et al. 1996; Kwon, 1996)

5 OSU IGS/LEO Associate Analysis Center LEO POD techniques currently supported Triple difference kinematic (OSU software) Dynamic (University of Texas, MSODP1) POD techniques under implementation at OSU Reduced dynamics Dynamic Measurements from current and future satellite missions Microlab 1, SAC-C, C, CHAMP, GRACE, COSMIC Atmospheric profiling

6 OSU IGS/LEO AAC Great Lakes GPS Network (by ) OSU 16-station network accepted by NSF/SuomiNet collocated at tide gauges Part of the NOAA CORS supported by other partners Additional 6 NOAA CORS stations at tide gauges The ultimate goal Combined use of ground-based and space borne observations for potential D atmospheric tomography Atmospheric profiling in near- real time Investigate possible enhancement of Great Lakes Forecasting SystemS

7 Great Lakes GPS Network: Tentative NGS CO-OPS FSL UCAR SuomiNet Stone Lab GFZ

8 Occultation Technique of WV Retrieval: Occultation Geometry

9 Occultation Technique of WV Retrieval Fundamental measurements GPS phase atmospheric excess during occultation Bending angle α derived from phase excess Refractivity N derived from bending angle by Abel inversion Orbital quality requirements LEO 3D velocity should be <.1 mm/s GPS orbits should be < cm Kinematic POD with 3 cm 3D RMS should allow T retrieval better than 1º 1 K up to 3- km

10 Estimated Temperature Error [K] Induced by the Errors in in GPS/LEO Orbits: Simulation Results (Langley, 1999; Zhao, 1998) LEO Altitude [km] Orbit Accuracy [cm] Post-processed (few days to weeks) GPS LEO <1 7.1 to km. to km.3 to km.1 to km Orbit Accuracy [cm] Rapid Solution (1- days) 1 <3.6 to km. to km.9 to km. to km Orbit Accuracy [cm] Predicted <1. to km.3 to km 3.6 to km.6 to km

11 GPS/MET Test Results Since SAC-C and CHAMP occultation data were not available, GPS/MET data were used for testing

12 Kinematic POD Advantages No gravity model error affects the solution Fast (potential for near-real real time) Quality solution for good PDOP Disadvantages No dynamics to compensate for bad geometry Requires correct coordinates for a starting No solution or weak solution for bad geometry

13 IGS reference stations and GPS orbit data or OSU GODIVA LEO observation data Binary local data base OSU Kinematic POD Station clock error estimation A priori values for LEO and station coordinates Detect cycle slips Construct triple phase differences Normal matrix Reduce normal matrix LEO orbit interpolation between s of observation LEO POD Solution and update of the a priori values

14 OSU Kinematic POD: Processing Model CS are detected using: 1 st and th differences of ionosperic residual phase/code geometry free linear combination double difference geometry/ionosphere free phase/range linear combination tested value: wideline ambiguity (Gao and Li, 1999) Least Squares Batch Solution approximated solution: DD range observations the optimal length of a batch is still investigated optimal base geometry is still investigated

15 Kinematic POD: CHAMP Test Data Test data set: 3 :3 pm, May 1, 1 Corresponds to approximately 1. revolution Data interval 1 s (3 s) CHAMP (IGS stations) Different station number tested The he best solution obtained for the test data set is for 66 stations elevation cut off º (CHAMP) and 1 º for the stations (next figure) double difference based CS detection

16 Kinematic Orbit: CHAMP

17 Kinematic Solution: Number of Satellites and Stations per Epoch arctic equator antarctic equator arctic equator antarctic 3 # of satellites # of stations Number

18 Kinematic Solution: Bad Geometry (1) number of satellites number of stations () number of satellites number of stations number of satellites 3 1 number of satellites 3 1 (3) number of stations () number of stations

19 Kinematic Solution: Bad Geometry (1) Number of satellites Number of stations () Number of satellites Number of stations (3) Number of satellites Number of stations () Number of satellites Number of stations

20 Kinematic Solution: Cycle Slips CHAT KELY WHIT YELL CHUR WILL UCLU DUBO ALGO NRC1 STJO WES CASA USNA SOL1 VNDP AOA1 GOLD MONP PIN1 PIE1 MDO1 BRMU MKEA KOUR AREQ EISL RIOG OHIG NYAL TROM KIRU YAKZ ONSA MAG ZWEN BRUS BOR1 GLSV ZIMM GRAS MADR POL SELE KIT3 XIAN USUD TSKB LHAS SHAO BAHR GUAM MALISEY1 COCO DARW KARR ALIC SUTH TIDB AUCK HOB KERG MAC1 MAW1 DAV1 CAS1 Blue square no CS Red square CS Section, 13 s

21 CS Detection Based on DD Phase/Range Combination ambiguity sd= s

22 CHAMP Kinematic POD: Effects of GPS Cycle Slips for -h h Data Station Cycle Slip Ratio(%) Station Cycle Slip Ratio(%) Based on ion residuals CHAMP ALGO FAIR KOSG MADR TIDB GOLD. TROM KOKB YELL 1.8 Based on DD phase/range combination (Gao & Li, 1999) % (66 stations)

23 Difference between RSO and Kinematic POD 1 X Y Z 8 6 Meter

24 3-D D difference between RSO and Kinematic POD 1 RMS Meter

25 Conclusion and Summary Presented analysis focus on CHAMP data quality for May 1, 1 Only relative fit to reference orbit shown Software modules to be added: body to inertial transformation offset correction from GPS receiver to CoM CS fixing? Additional study needed to determine an optimal processing batch length and the number of stations Implementation of DD technique?