Evaluation of Scintillation Mitigation Using PLL and DLL Tracking Jitter Models in a Multi-GNSS Kinematic Precise Point Positioning Model
Brian Weaver, Marcio Aquino, Sreeja Vadakke Veettil, Lei Yang, Kai Guo, University of Nottingham, UK
Location: Pavilion Ballroom West
Alternate Number 2
The European Galileo Global Navigation Satellite System (GNSS) is expected to be fully deployed by the year 2020 with 30 operational satellites (Pan et al. 2019). This addition to the existing GPS and GLONASS (GLO) constellations will further enhance high accuracy multi-GNSS positioning and navigation capabilities with improved satellite availability, better satellite sky distribution and benefits from the interference resistant Galileo E5 (AltBOC) signal. For example, kinematic precise point positioning (PPP) accuracy improvements of more than 25% and 10% were achieved by Xia et al. (2019) when Galileo E1/E5a measurements were included in GPS-only and GPS/GLO models, respectively. Precise point positioning enables cm-level positioning accuracy under ideal conditions for dual-frequency single-receiver GNSS users, where precise network-estimated orbit and clock information is constrained in the user model (Malys and Jensen 1990, Zumberge et al. 1997; Kouba and Heroux 2001). However, ionospheric scintillation conditions can amplify positioning errors to orders of magnitude larger than non-scintillation conditions. The ionospheric scintillation effects on GPS positioning were successfully mitigated by Aquino et al. (2009) and Silva et al. (2010), where tracking errors estimated based on Conker et al. (2003) scintillation sensitive tracking models were used to improve the GNSS stochastic model. This contribution exploits both Galileo measurements and the scintillation mitigation approach proposed by Aquino et al. (2009) in a multi-GNSS GPS/Galileo kinematic PPP model.
This study uses scintillation and GNSS measurement data collected at the high-latitude station named LYB (78° 10’ N, 15° 59’ E) in Svalbard, on September 1, 2019, by an ionospheric scintillation monitoring receiver (ISMR). Station LYB is equipped with a Septentrio PolaRxS receiver that outputs amplitude (S4) and phase scintillation (sigmaPhi) indices, high-rate GNSS measurement data and post correlation in-phase (I) and quadra-phase (Q) samples. In this study, the scintillation mitigation approach uses I/Q data output by the ISMR to calculate receiver phase lock loop (PLL) tracking jitters with delay lock loop (DLL) tracking jitters assumed to equal scaled PLL tracking jitters. The S4 and sigmaPhi scintillation indices were calculated at each second for observed GPS L1 and Galileo E1 signals from satellites above 7-degrees in elevation. Then, all S4 and sigmaPhi values were classified with the following thresholds: weak scintillation when index ? 0.3, moderate scintillation when 0.3 < index ? 0.6 and strong scintillation when index > 0.6. The most active scintillation period was identified using the classified scintillation indices to begin at approximately 18:00 UTC and end at approximately 20:30 UTC. In this period, 278 GPS L1 measurements and 250 Galileo E1 measurements were affected by strong phase scintillation while only 37 combined GPS L1 and Galileo E1 measurements were affected by strong amplitude scintillation.
Kinematic PPP solutions were calculated using the University of Nottingham POINT software for 1- and 60-second GPS/Galileo measurements on September 1, 2019 from 17:30 to 20:30 UTC. The 1- and 60-second data were processed first with a common elevation-based weighting to evaluate non-mitigated performance, then again with the weighting matrix populated by I/Q tracking jitters. The PPP processing began at 17:30 to allow time for solution convergence before the start of the strong phase scintillation conditions at 18:00 UTC. All solutions were constrained to International GNSS Service (IGS) Multi-GNSS Experiment (MGEX) final orbit and clock products and used undifferenced, ionosphere-free combinations of dual-frequency code and carrier phase measurements in an extended Kalman filter to estimate three-dimensional coordinates, carrier phase ambiguities, the residual zenith tropospheric delay and one receiver clock parameter per GNSS constellation. Solutions with GPS measurements used the L1 and L2 frequencies while Galileo solutions used the E1 and E5 frequencies. Kalman filter measurement-minus-computed residuals were used to detect outliers with, at most, 50% of estimated ambiguities allowed as outliers before a complete ambiguity reset is issued. Additional models were applied to correct for earth’s polar motion, solid earth tides, receiver and satellite antenna corrections, phase wind up and relativistic effects. Receiver clock jumps are necessary to constrain clock drift but cause errors for Kalman filter processing where the dynamic model is unable to predict these jumps. To eliminate clock reset errors, the receiver clock error was calculated in a least-squares solution at each epoch. The least-squares clock estimate was then used to seed the Kalman filter and a low process noise was used for the estimated clock parameter.
A 24-hour RINEX file was submitted to the Natural Resources Canada online PPP tool and processed in static PPP mode to estimate “ground truth” coordinates. Positioning errors are defined as the difference between the final static coordinates estimated by the online tool and the kinematic coordinates estimated by POINT at each epoch. The performance of each solution was evaluated using the root mean square (RMS) for both the local 3D and up coordinate errors. Although individual carrier phase ambiguities were reset by the outlier detection algorithm, the 1-second elevation and 1-second and 60-second tracking jitter solutions were free from complete ambiguity estimation resets. The 60-second elevation solution contained one complete ambiguity reset at 18:48 UTC which caused mid-data re-convergence and up coordinate errors of up to 3 m in the following three epochs. At the same epoch, the 60-second tracking jitter solution successfully identified and reset 30% of the total estimated ambiguities to avoid a complete reset and maintain cm-level accuracy. The 1-second elevation and tracking jitter solutions were nearly identical, with the up error RMS calculated as 8.2 cm and 8.1 cm, respectively, and 3D error RMS equal to 13.5 cm and 13.8 cm, respectively. The 60-second tracking jitter approach improved 3D and up error RMS by 82% and 68%, respectively, compared to the non-mitigated elevation weighting technique. For the 60-second elevation and tracking jitter solutions, the up error RMS was 26.0 cm and 8.3 cm, respectively, and the 3D error RMS was 90.6 cm and 16.2 cm, respectively. The 3D and up error RMS for the 60-second tracking jitter solution are comparable to both the elevation and tracking jitter 1-second solutions with a maximum RMS difference of 2.7 cm in 3D error compared with the 1-second elevation approach.
Scintillation indices output by a high-latitude ISMR were studied to identify a strong phase scintillation occurrence on September 1, 2019. Ionospheric scintillation mitigation with I/Q tracking jitters incorporated in the positioning stochastic model was compared to an elevation-based weighting method for combined GPS/Galileo kinematic PPP processing at 1- and 60-second measurement intervals respectively. The performance of mitigated and non-mitigated solutions was evaluated using local 3D and up coordinate error RMS and revealed nearly identical performance for 1-second processing. For the 60-second solutions, the tracking jitter technique improved 3D RMS error by 82% compared with the elevation approach due to large errors in the epochs following a complete ambiguity reset in the elevation-based solution.
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