Kinematic Precise Point Positioning Algorithm Development and Improvements using External Atmospheric Information

Weaver, Brian J. (2023) Kinematic Precise Point Positioning Algorithm Development and Improvements using External Atmospheric Information. PhD thesis, University of Nottingham.

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Precise point positioning (PPP) is a high-accuracy GNSS positioning technique used to process single-receiver, dual-frequency carrier-phase and pseudorange measurements using precise network-estimated satellite clock and orbit data products, along with optional satellite carrier phase bias and attitude information. The PPP strategy does not require any nearby reference stations and has therefore gained interest in many commercial and scientific industries over the past few decades. However, kinematic PPP can be affected by large positioning errors in the presence of ionospheric scintillation, under strong ionospheric gradients, and during strong tropospheric storm events. Therefore, this thesis aims to develop new methods that incorporate additional external atmospheric information into the positioning model to improve kinematic PPP accuracy under these harsh atmospheric conditions.

Ionospheric scintillation of GNSS signals is caused by plasma density irregularities in the ionosphere and is characterized by rapid phase and amplitude fluctuations of the received signal. In equatorial regions, between ±20° geomagnetic latitude, strong and frequent post-sunset scintillation is common and can amplify positioning errors by several orders of magnitude. However, an increased number of satellites using modernized signals could help to mitigate this impact. Therefore, this thesis evaluates kinematic PPP performance using multi-GNSS processing under low latitude ionospheric scintillation conditions. Compared to GPS-only processing, multi-GNSS configurations using Galileo measurements achieved respective average vertical positioning accuracy and precision improvements equal to 3.4-cm (39.8%) and 1.8-cm (52.7%). In addition, multi-GNSS configurations improved daily respective horizontal and vertical position accuracy and precision by up to 13.0-cm (80.4%) and 13.6-cm (90.4%) during the worst GPS-only processing day.

Although multi-GNSS processing can improve kinematic PPP performance under ionospheric scintillation conditions, a non-mitigated satellite elevation-based stochastic model degrades positioning accuracy when high-elevation satellites are affected by moderate or strong scintillation. Furthermore, scintillation mitigation using receiver tracking error outputs in a modified stochastic model is affected by frequent outages under strong scintillation conditions and has only been demonstrated for single-system processing. Therefore, this thesis develops repaired and normalized multi-GNSS receiver tracking error model outputs to respectively increase mitigation availability and expand mitigation benefits to non-specialized users that may require a mixed stochastic approach. The proposed techniques were evaluated using GPS+Galileo measurement processing for a common geodetic receiver under moderate and strong low latitude ionospheric scintillation conditions. Relative to a standard elevation-based stochastic model, the mitigated approach improved the daily worst-case 3D kinematic PPP error by 16.6-cm (46.7%) and 13.6-cm (37.4%) for the two best cases, while the average 3D position error for both stochastic methods agreed at the cm-level in all cases.

Tropospheric effects are typically addressed in GNSS processing by a priori hydrostatic correction models and estimation of zenith wet delay and horizontal gradient components. However, rapid changes in atmospheric water vapor caused by heavy rainfall can amplify tropospheric asymmetry effects and reduce kinematic PPP accuracy due to tightly constrained tropospheric parameters. Therefore, this thesis develops and evaluates deterministic, partially stochastic, and fully stochastic correction methods that use progressively more GNSS network-estimated tropospheric data under extreme tropospheric storm conditions to improve the achievable kinematic PPP accuracy at user locations. Comparison with the non-corrected model revealed that the fully stochastic approach improved the hourly horizontal and vertical position error by up to 3.2-cm (45.5%) and 10.2-cm (66.2%), respectively, while deterministic and partially stochastic methods improved only the vertical positioning error component.

Increased ionospheric activity for high-elevation satellites can amplify otherwise stable positioning errors in an elevation-based stochastic model unless the stochastic model is modified with user-estimated ionospheric delay information to amplify measurement noise. However, this technique relies on continuous dual-frequency carrier phase measurements that are assumed to be free from cycle slip effects, which is not guaranteed in challenging ionospheric environments due to measurement outages and poor-quality carrier phase data. Therefore, this thesis suggests an alternative stochastic model strategy to amplify measurement noise using the rate of the ionospheric delay computed from external global and regional ionospheric map products that are independent of cycle slips and outages that a GNSS user may experience. For low latitude stations evaluated relative to a standard satellite elevation-based stochastic model, the proposed technique successfully improved maximum 3D kinematic PPP error by up to 15.6-cm (52.5%) when the global ionospheric map product was used. Extreme variability of the experimental 60-second update rate regional ionospheric map data deactivated the modified stochastic approach for 88.8% of epochs which resulted in positioning performance identical to the elevation-based method at the mm-level.

Item Type: Thesis (University of Nottingham only) (PhD)
Supervisors: Yang, Lei
Blunt, Paul
Aquino, Marcio
Veettil, Sreeja
Keywords: GNSS, PPP, High-accuracy positioning, Challenging atmospheric conditions
Subjects: T Technology > TA Engineering (General). Civil engineering (General) > TA 501 Surveying
Faculties/Schools: UK Campuses > Faculty of Engineering > Department of Civil Engineering
Item ID: 74067
Depositing User: Weaver, Brian
Date Deposited: 17 Apr 2024 10:04
Last Modified: 17 Apr 2024 10:04

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