Robust Positioning in the Presence of Multipath and NLOS GNSS Signals

GNSS signals can be blocked and reflected by nearby objects, such as buildings, walls, and vehicles. They can also be reflected by the ground and by water. These effects are the dominant source of GNSS positioning errors in dense urban environments, though they can have an impact almost anywhere. Non- line-of-sight (NLOS) reception occurs when the direct path from the transmitter to the receiver is blocked and signals are received only via a reflected path. Multipath interference occurs, as the name suggests, when a signal is received via multiple paths. This can be via the direct path and one or more reflected paths, or it can be via multiple reflected paths. As their error characteristics are different, NLOS and multipath interference typically require different mitigation techniques, though some techniques are applicable to both. Antenna design and advanced receiver signal processing techniques can substantially reduce multipath errors. Unless an antenna array is used, NLOS reception has to be detected using the receiver's ranging and carrier-power-to-noise-density ratio (C/N0) measurements and mitigated within the positioning algorithm. Some NLOS mitigation techniques can also be used to combat severe multipath interference. Multipath interference, but not NLOS reception, can also be mitigated by comparing or combining code and carrier measurements, comparing ranging and C/N0 measurements from signals on different frequencies, and analyzing the time evolution of the ranging and C/N0 measurements

Investigation of Shadow Matching for GNSS Positioning in Urban Canyons

All travel behavior of people in urban areas relies on knowing their position. Obtaining position has become increasingly easier thanks to the vast popularity of ‘smart’ mobile devices. The main and most accurate positioning technique used in these devices is global navigation satellite systems (GNSS). However, the poor performance of GNSS user equipment in urban canyons is a well-known problem and it is particularly inaccurate in the cross-street direction. The accuracy in this direction greatly affects many applications, including vehicle lane identification and high-accuracy pedestrian navigation. Shadow matching is a new technique that helps solve this problem by integrating GNSS constellation geometries and information derived from 3D models of buildings. This study brings the shadow matching principle from a simple mathematical model, through experimental proof of concept, system design and demonstration, algorithm redesign, comprehensive experimental tests, real-time demonstration and feasibility assessment, to a workable positioning solution. In this thesis, GNSS performance in urban canyons is numerically evaluated using 3D models. Then, a generic two-phase 6-step shadow matching system is proposed, implemented and tested against both geodetic and smartphone-grade GNSS receivers. A Bayesian technique-based shadow matching is proposed to account for NLOS and diffracted signal reception. A particle filter is designed to enable multi-epoch kinematic positioning. Finally, shadow matching is adapted and implemented as a mobile application (app), with feasibility assessment conducted. Results from the investigation confirm that conventional ranging-based GNSS is not adequate for reliable urban positioning. The designed shadow matching positioning system is demonstrated complementary to conventional GNSS in improving urban positioning accuracy. Each of the three generations of shadow matching algorithm is demonstrated to provide better positioning performance, supported by comprehensive experiments. In summary, shadow matching has been demonstrated to significantly improve urban positioning accuracy; it shows great potential to revolutionize urban positioning from street level to lane level, and possibly meter level

A Long-Term Broadcast Ephemeris Model for Extended Operation of GNSS Satellites

GNSS positioning relies on orbit and clock information, which is predicted on ground and transmitted by the individual satellites as part of their broadcast navigation message. The predictions are typically refreshed at least once per day and partitioned into short-arc ephemeris data sets covering a representative validity period of 0.5-4 h. For an increased autonomy of either the space or user segment, the capability to predict a GNSS satellite orbit over extended periods of up to two weeks is studied. A tailored force model for numerical orbit propagation is proposed that offers high accuracy but can still be used in real-time environments. Aside from Earth-orientation parameters, six state vector components and three empirical solar radiation pressure parameters are employed for each satellite and adjusted to past orbits. Using the Galileo constellation with its high-grade hydrogen maser clocks as an example, global average signal-in-space range errors of less than 25 m RMS and 3D position errors of less than about 50 m are demonstrated after two week predictions in 95% of all test cases over a half-year period. The autonomous orbit prediction model thus enables adequate quality for a rapid first fix or contingency navigation in case of lacking ground segment updates

Estimating Earth's temporal gravity field from GRACE observations: Mitigation of thermal errors and the interplay between orbital characteristics, basis functions and spatial resolution

The Gravity Recovery and Climate Experiment (GRACE) mission measured the combined effect of the Earth's static and time-variable gravity fields globally and near-continuously over 15 years at unprecedented accuracy. Launched in 2002, the GRACE mission used a unique low-Earth orbit satellite-to-satellite tracking mission design. The time-variable gravity field is influenced by the movement of masses within the hydrosphere and the solid Earth. By directly monitoring mass balance changes due to flood, drought, groundwater extraction, ocean circulation, ocean mass increase and ice mass loss, results from the GRACE mission have increased understanding of the impacts of human activity, natural variation and climate change. The accuracy of GRACE estimates of the time-variable gravity field and the associated mass anomaly time series is affected by several factors. These include orbital characteristics, quality of the observations and background forcing models and the processing strategies used for precise orbit determination and temporal gravity field estimation. This study aims to improve GRACE-based estimates of the time-variable gravity field to analyse mass anomalies by mitigating measurement errors and optimising the choices of processing strategies. The precise calculation of GRACE satellite orbits is reliant on knowledge of accurate non-gravitational forces acting on the spacecraft. Optimal performance of the accelerometers requires a highly stable thermally controlled environment which was not maintained throughout the mission. In this study, I developed pre-processing and calibration strategies to account for thermally-induced errors in the non-gravitational acceleration measurements. Accurate time-variable gravity models could then be estimated from GRACE data even in the presence of thermally-induced error. Some mathematical form, or basis function, must be assumed to parameterise the temporal gravity field on the surface of Earth. The choice of the inter-satellite observation and basis functions can also improve the recovery of the gravity field by better localising the mass variations. This study demonstrates how mass concentration (mascon) tiles can reduce signal leakage and intra-mascon variability (the variations of mass change signals within a mascon). I identified the optimal mascon parameterisation through simulation, subsequently used to generate the ANU GRACE mass anomaly time series. Improved localisation of the mass variation signals was achieved using the range acceleration as the inter-satellite observation rather than the conventional approach of using range rate observations. The GRACE processing strategies chosen to optimise the accuracy of the temporal gravity solutions tend to be used - without change - across the mission's duration. However, as the geometry of the orbits of the twin spacecraft vary throughout the mission, the ability of the observations to recover high-frequency spatial signals also varies. Through simulation, I assessed the impact of the changing orbital elements on the spatial resolution of the GRACE mascon solutions as a function of altitude and ground track density. With appropriate regularisation, mascons as small as $\sim$150 $\times$ 150 km yield the most accurate solutions even during periods of orbit resonance. Under realistic simulation conditions, the temporal gravity field solutions are significantly improved with decreased orbit altitude. Many components of my work have been implemented into the ANU GRACE software, including pre-processing and calibration strategies that account for thermally-induced errors in the accelerometer measurements, filtering to mitigate high-frequency inter-satellite range acceleration noise, protocols to create mascon grids and the iteration procedure used to generate the ANU GRACE mass anomaly estimates. The results show substantial seasonal variations, ice sheet mass loss and global mean sea level increase consistent with previous studies

GNSS precise point positioning :the enhancement with GLONASS

PhD ThesisPrecise Point Positioning (PPP) provides GNSS navigation using a stand-alone receiver with no base station. As a technique PPP suffers from long convergence times and quality degradation during periods of poo satellite visibility or geometry. Many applications require reliable realtime centimetre level positioning with worldwide coverage, and a short initialisation time. To achieve these goals, this thesis considers the use of GLONASS in conjunction with GPS in kinematic PPP. This increases the number of satellites visible to the receiver, improving the geometry of the visible satellite constellation. To assess the impact of using GLONASS with PPP, it was necessary to build a real time mode PPP program. pppncl was constructed using a combination of Fortran and Python to be capable of processing GNSS observations with precise satellite ephemeris data in the standardised RINEX and SP3 formats respectively. pppncl was validated in GPS mode using both staticsites and kinematic datasets.In GPS only mode,one sigma accuracy of 6.4mm and 13mm in the horizontal and vertical respectively for 24h static positioning was seen. Kinematic horizontal and vertical accuracies of 21mm and 33mm were demonstrated. pppncl was extended to assess the impact of using GLONASS observations in addi- tion to GPS instatic and kinematic PPP. Using ESA and Veripos Apex G2 satel- lite orbit and clock products,the average time until 10cm 1D static accuracy was achieved, over arange of globally distributed sites, was seen to reduce by up to 47%. Kinematic positioning was tested for different modes of transport using real world datasets. GPS/GLONAS SPPP reduced the convergence time to decimetre accuracy by up to a factor of three. Positioning was seen to be more robust in comparison to GPS only PPP, primarily due to cycle slips not being present on both satellite systems on the occasions when they occurred,and the reduced impact of undetected outliersEngineering and Physical Sciences Research Council, Verip os/Subsea

Multi-Constellation GNSS Performance Evaluation for Urban Canyons Using Large Virtual Reality City Models

Positioning using the Global Positioning System (GPS) is unreliable in dense urban areas with tall buildings and/or narrow streets, known as ‘urban canyons’. This is because the buildings block, reflect or diffract the signals from many of the satellites. This paper investigates the use of 3-Dimensional (3-D) building models to predict satellite visibility. To predict Global Navigation Satellite System (GNSS) performance using 3-D building models, a simulation has been developed. A few optimized methods to improve the efficiency of the simulation for real-time purposes were implemented. Diffraction effects of satellite signals were considered to improve accuracy. The simulation is validated using real-world GPS and GLObal NAvigation Satellite System (GLONASS) observations. The performance of current and future GNSS in urban canyons is then assessed by simulation using an architectural city model of London with decimetre-level accuracy. GNSS availability, integrity and precision is evaluated over pedestrian and vehicle routes within city canyons using different combinations of GNSS constellations. The results show that using GPS and GLONASS together cannot guarantee 24-hour reliable positioning in urban canyons. However, with the addition of Galileo and Compass, currently under construction, reliable GNSS performance can be obtained at most, but not all, of the locations in the test scenarios. The modelling also demonstrates that GNSS availability is poorer for pedestrians than for vehicles and verifies that cross-street positioning errors are typically larger than along-street due to the geometrical constraints imposed by the buildings. For many applications, this modelling technique could also be used to predict the best route through a city at a given time, or the best time to perform GNSS positioning at a given location

User Guide to the PDS Dataset for the Cassini Composite Infrared Spectrometer (CIRS)

This User Guide to the Cassini Composite Infrared Spectrometer (CIRS) has been written with two communities in mind. First and foremost, scientists external to the Cassini Project who seek to use the CIRS data as archived in the Planetary Data System (PDS). In addition, it is intended to be a comprehensive reference guide for those internal to the CIRS team

The GLAS Algorithm Theoretical Basis Document for Precision Orbit Determination (POD)

The Geoscience Laser Altimeter System (GLAS) was the sole instrument for NASA's Ice, Cloud and land Elevation Satellite (ICESat) laser altimetry mission. The primary purpose of the ICESat mission was to make ice sheet elevation measurements of the polar regions. Additional goals were to measure the global distribution of clouds and aerosols and to map sea ice, land topography and vegetation. ICESat was the benchmark Earth Observing System (EOS) mission to be used to determine the mass balance of the ice sheets, as well as for providing cloud property information, especially for stratospheric clouds common over polar areas. The GLAS instrument operated from 2003 to 2009 and provided multi-year elevation data needed to determine changes in sea ice freeboard, land topography and vegetation around the globe, in addition to elevation changes of the Greenland and Antarctic ice sheets. This document describes the Precision Orbit Determination (POD) algorithm for the ICESat mission. The problem of determining an accurate ephemeris for an orbiting satellite involves estimating the position and velocity of the satellite from a sequence of observations. The ICESatGLAS elevation measurements must be very accurately geolocated, combining precise orbit information with precision pointing information. The ICESat mission POD requirement states that the position of the instrument should be determined with an accuracy of 5 and 20 cm (1-s) in radial and horizontal components, respectively, to meet the science requirements for determining elevation change