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Session B3: Future Trends in GNSS Augmentation Systems

PPP and Galileo High Accuracy Service with Satellite Selection Strategies for Kinematic Applications
Ilaria Martini, Technical Advisor for the European Commission; Melania Susi, European Commission, Joint Research Centre (JRC); Ignacio Fernandez-Hernandez, European Commission
Date/Time: Thursday, Sep. 22, 8:57 a.m.

High accuracy service and Precise Point Positioning (PPP) are interesting not only for static users or geodetic applications but also for kinematic ones as for example autonomous transportations in UAVs, rail, road and maritime sectors.
The Galileo High Accuracy Service (HAS) will serve these applications with free of charge PPP corrections transmitted worldwide. In 2021 the testing phase started, and the initial service is expected in 2022 providing to these user communities the possibility to satisfy their demanding high accuracy needs [1].
With these techniques the user receives the information through the GNSS signals (E6 in the Galileo HAS) and it doesn’t need connectivity to a ground station or to GEO satellites: a good coverage is therefore ensured also in harsh environments.
On the other side, high accuracy user needs are often coupled with other stringent requirements, such as continuity, integrity and short convergence times. This paper analyses the benefits of PPP and Galileo HAS in these applications and it proposes a solution based on satellite selection and pre-processing strategies at the receiver to address the challenges faced by the users in the most difficult environments.
With the improvement of constellation performance over the past years [2] [3] [4], the contribution of satellite orbit and clock errors is reduced with respect to other error components. With PPP solutions like HAS, this contribution is further reduced, both in nominal [5] and non-nominal cases [6]. The receiver can take conservative models on the satellite errors to cover the system contribution and develop threats detection and mitigation techniques to address local errors.
In addition, propagation errors and local effects dominate and previous works showed the need to have accurate modelling of the error sources [7] [8], integration with other technologies and multisensor fusion [9]. Furthermore, sequential positioning with a Kalman filter can exploit satellite motion [10], take into account temporal correlation effects [11] and have accurate bounding [12] [13].
This multiconstellation and multifrequency scenario is characterized by a large number of signals and satellites in some cases combined with multisensor measurements. Rather than using all in view satellites and measurements and performing a posteriori fault detection and exclusion, the receiver can assess the benefits and drawbacks of properly selecting the best subset of measurements. In fact, the position with three constellations reaches satisfactory accuracy with about half of the satellites in view and including additional signals brings a marginal and negligible performance improvement and in some cases degradation of performance [14].
The convergence and reconvergence time of the Kalman filter represents a challenge considering in particular real-time needs, cycle slips, and phase instabilities typical of harsh environment. Screening out a priori faulty or degraded measurements allows to avoid filter reinitialization and improve continuity performance.
In addition, integrity algorithms based on solution separation or bank of Kalman filters (e.g. Advanced RAIM [15] [16]) have a high computation load which can be prohibitive for no aviation receivers with miniaturized chipsets (e.g. small UAVs, drones). Selection strategies can allow the receiver to process the signals which are strictly needed for the intended operation and reduce the computation load.
The selection strategy to be presented in this paper performs screening of degraded or low-quality signals and identifies the best subset of measurements to optimize the performance focusing not only on accuracy but considering also continuity risk and receiver computation load.
The paper provides, first of all, a characterization of the HAS performance showing the accuracy of the satellite orbit and clock after application of the HAS corrections. Previous characterizations of the HAS performance [6] will be expanded to include aspects relevant for sequential filter solutions (phase stability, time correlation), continuity and integrity performance (error bounding, distribution tails, data gaps) which are important for the receiver to characterize the PPP errors and design conservative models.
The user PPP performance will be analysed in terms of accuracy and continuity (e.g. data gaps, convergence time) showing in particular the benefits of satellite selection strategies. Several screening and preprocessing algorithms based at least on Signal to Noise Ratio, phase quality monitoring, cycle slip detectors, code minus carrier, Open Service and HAS flags and HAS correction monitoring, will be analyzed and selected to assess the measurement quality and identify the advantages and risks of including or excluding measurements for the positioning. Furthermore, the criteria to identify the optimum subset will be presented describing cost functions for the continuity risk and computation load which are used to weight the different measurement sets.
An analysis with automotive real data collected at the Joint Research Center (JRC, Ispra) will assess the benefit of Galileo HAS and PPP for kinematic applications, including harsh environments, and autonomous transportation in general. It will test the proposed pre-processing and selection strategies to show whether the user can reach sufficient accuracy with a reduced subset of signals, and whether selecting the most stable, nominal, and reliable ones improves the performance and mitigates continuity risk and computation load.
References
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[2] W. Todd and J. Blanch, "Characterization of GPS Clock and Ephemeris Errors to Support ARAIM," in ION 2015 Pacific PNT Meeting, 2015.
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[10] M. Joerger and B. Pervan, "Multi-constellation ARAIM exploiting satellite motion," Journal of the Institute of Navigation, 67(2), pp. 235-253, 2020.
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[13] O. Julien, R. Bryant and C. Hide, "Tight Position Bounding for Automotive Integrity," Inside GNSS, 2021.
[14] D. Gerbeth, I. Martini, M. Rippl and M. Felux, "Satellite Selection Methodology for Horizontal Navigation and Integrity Algorithms," in ION GNSS+ 2016, 2016.
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[16] K. Gunning, J. Blanch, T. Walter, L. De Groot and L. Norman, "Design and Evaluation of Integrity Algorithms for PPP in Kinematic Applications,," in 31st International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2018), Miami (Florida), 2018.



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