Multi-frequency and multi-GNSS PPP phase bias estimation and ambiguity resolution

Multi-frequency and multi-GNSS measurements from modernized satellites are properly integrated for PPP with ambiguity resolution to achieve the state-of-the-art fast and accurate positioning, which provides an important contribution to GNSS precise positioning and applications. The multi-frequency and multi-GNSS PPP phase bias estimation and ambiguity resolution, which is accomplished by a unified model based on the uncombined PPP, are thoroughly evaluated with special focus on Galileo and BDS

Multi-GNSS integer ambiguity resolution enabled precise positioning

In this PhD thesis multi-Global Navigation Satellite System (GNSS) positioning results when combining the American Global Positioning System (GPS), Chinese BeiDou Navigation Satellite System (BDS), European Galileo and Japanese Quasi-Zenith Satellite System (QZSS) will be presented. The combined systems will be evaluated in comparison to the single-systems, for short (atmosphere-fixed) to long (atmosphere-present) baselines. It will be shown that the combined systems can provide for improved integer ambiguity resolution and positioning performance over the single-systems

Multi-frequency and multi-GNSS PPP phase bias estimation and ambiguity resolution

Multi-frequency and multi-GNSS measurements from modernized satellites are properly integrated for PPP with ambiguity resolution to achieve the state-of-the-art fast and accurate positioning, which provides an important contribution to GNSS precise positioning and applications. The multi-frequency and multi-GNSS PPP phase bias estimation and ambiguity resolution, which is accomplished by a unified model based on the uncombined PPP, are thoroughly evaluated with special focus on Galileo and BDS

A Kalman filter single point positioning for maritime applications using a smartphone

Different positioning techniques have been largely adopted for maritime applications that require high accuracy kinematic positioning. The main objective of the paper is the performance assessment of a Single Point Positioning algorithm (SPP), with a Kalman filter (KF) estimator, adapted for maritime applications. The KF has been chosen as estimation technique due to the ability to consider both the state vector dynamic and the measurements. Particularly, in order to compute an accurate vertical component of the position, suitable for maritime applications, the KF settings have been modified by tuning the covariance matrix of the process noise. The algorithm is developed in Matlab environment and tested using multi-GNSS single-frequency raw data, collected by a smartphone located on board a moving ship. The algorithm performance evaluation is carried out in position domain and the results show an enhancement of meter order on vertical component compared to the classical SPP based on Least Square estimation technique. In addition, different GNSSs configurations are considered to verify the benefits of their integration in terms of accuracy, solution availability and geometry

GPS/GLONASS carrier phase elevation-dependent stochastic modelling estimation and its application in bridge monitoring

The Global Positioning System (GPS) based monitoring technology has been recognised as an essential tool in the long-span bridge health monitoring throughout the world in recent years. However, the high observation noise is still a big problem that limits the high precision displacement extraction and vibration response detection. To solve this problem, GPS double-difference model and many other specific function models have been developed to eliminate systematic errors e.g. unmodeled atmospheric delays, multipath effect and hardware delays. However, relatively less attention has been given to the noise reduction in the deformation monitoring area. In this paper, we first proposed a new carrier phase elevation-dependent precision estimation method with Geometry-Free (GF) and Melbourne-Wü bbena (MW) linear combinations, which is appropriate to regardless of Code Division Multiple Access (CDMA) system (GPS) or Frequency Division Multiple Access (FDMA) system (GLONASS). Then, the method is used to estimate the receiver internal noise and the realistic GNSS stochastic model with a group of zero-baselines and short-baselines (served for the GNSS and Earth Observation for Structural Health Monitoring of Bridges (GeoSHM) project), and to demonstrate their impacts on the positioning. At last, the contribution of integration of GPS and GLONASS is introduced to see the performance of noise reduction with multi-GNSS. The results show that the higher level receiver internal noise in cost effective receivers has less influences on the short-baseline data processing. The high noise effects introduced by the low elevation satellite and the geometry variation caused by rising and dropping satellites, can be reduced by 10–20% with the refined carrier phase elevation-dependent stochastic model. Furthermore, based on observations from GPS and GLONASS with the refined stochastic model, the noise can be reduced by 30–40%, and the spurious signals in the real-life bridge displacements tend to be completely eliminated

BDS GNSS for Earth Observation

For millennia, human communities have wondered about the possibility of observing phenomena in their surroundings, and in particular those affecting the Earth on which they live. More generally, it can be conceptually defined as Earth observation (EO) and is the collection of information about the biological, chemical and physical systems of planet Earth. It can be undertaken through sensors in direct contact with the ground or airborne platforms (such as weather balloons and stations) or remote-sensing technologies. However, the definition of EO has only become significant in the last 50 years, since it has been possible to send artificial satellites out of Earth’s orbit. Referring strictly to civil applications, satellites of this type were initially designed to provide satellite images; later, their purpose expanded to include the study of information on land characteristics, growing vegetation, crops, and environmental pollution. The data collected are used for several purposes, including the identification of natural resources and the production of accurate cartography. Satellite observations can cover the land, the atmosphere, and the oceans. Remote-sensing satellites may be equipped with passive instrumentation such as infrared or cameras for imaging the visible or active instrumentation such as radar. Generally, such satellites are non-geostationary satellites, i.e., they move at a certain speed along orbits inclined with respect to the Earth’s equatorial plane, often in polar orbit, at low or medium altitude, Low Earth Orbit (LEO) and Medium Earth Orbit (MEO), thus covering the entire Earth’s surface in a certain scan time (properly called ’temporal resolution’), i.e., in a certain number of orbits around the Earth. The first remote-sensing satellites were the American NASA/USGS Landsat Program; subsequently, the European: ENVISAT (ENVironmental SATellite), ERS (European Remote-Sensing satellite), RapidEye, the French SPOT (Satellite Pour l’Observation de laTerre), and the Canadian RADARSAT satellites were launched. The IKONOS, QuickBird, and GeoEye-1 satellites were dedicated to cartography. The WorldView-1 and WorldView-2 satellites and the COSMO-SkyMed system are more recent. The latest generation are the low payloads called Small Satellites, e.g., the Chinese BuFeng-1 and Fengyun-3 series. Also, Global Navigation Satellite Systems (GNSSs) have captured the attention of researchers worldwide for a multitude of Earth monitoring and exploration applications. On the other hand, over the past 40 years, GNSSs have become an essential part of many human activities. As is widely noted, there are currently four fully operational GNSSs; two of these were developed for military purposes (American NAVstar GPS and Russian GLONASS), whilst two others were developed for civil purposes such as the Chinese BeiDou satellite navigation system (BDS) and the European Galileo. In addition, many other regional GNSSs, such as the South Korean Regional Positioning System (KPS), the Japanese quasi-zenital satellite system (QZSS), and the Indian Regional Navigation Satellite System (IRNSS/NavIC), will become available in the next few years, which will have enormous potential for scientific applications and geomatics professionals. In addition to their traditional role of providing global positioning, navigation, and timing (PNT) information, GNSS navigation signals are now being used in new and innovative ways. Across the globe, new fields of scientific study are opening up to examine how signals can provide information about the characteristics of the atmosphere and even the surfaces from which they are reflected before being collected by a receiver. EO researchers monitor global environmental systems using in situ and remote monitoring tools. Their findings provide tools to support decision makers in various areas of interest, from security to the natural environment. GNSS signals are considered an important new source of information because they are a free, real-time, and globally available resource for the EO community

Investigating multi-GNSS performance in the UK and China based on a zero-baseline measurement approach

GPS is the positioning tool of choice for a wide variety of applications where accurate (cm level or less) positions are required. However GPS is susceptible to a variety of errors that degrade both the quality of the position solution and the availability of these solutions. The contribution of additional observations from other GNSS systems may improve the quality of the positioning solution. This study investigates the contribution of the GLONASS and BeiDou systems and the potential improvement to the precision achieved compared to positioning using GPS only measurements. Furthermore, it is investigated whether the combination of the satellite systems can limit the noise level of the GPS-only solution. A series of zero-baseline measurements, of 1 Hz sampling rate, were recorded with different types of pairs of receivers over 12 consecutive days in the UK and in China simultaneously. The novel part in this study is comparing the simultaneous GNSS real measurements recorded in the UK and China. Moreover, the correlation between the geometry and positional precision was investigated. The results indicate an improvement in a multi-GNSS combined solution compared to the GPS-only solution, especially when the GPS-only solution derives from weak satellite geometry, or the GPS-only solution is not available. Furthermore, all the outliers due to poor satellite coverage with the individual solutions are limited and their precision is improved, agreeing also with the improvement in the mean of the GDOP, i.e. the mean GDOP was improved from 3.0 for the GPS only solution to 1.8 for the combined solution. However, the combined positioning did not show significant positional improvement when GPS has a good geometry and availability

A unified model for multi-frequency PPP ambiguity resolution and test results with Galileo and BeiDou triple-frequency observations

With the modernization of Global Navigation Satellite System (GNSS), triple- or multi-frequency signals have become available from more and more GNSS satellites. The additional signals are expected to enhance the performance of precise point positioning (PPP) with ambiguity resolution (AR). To deal with the additional signals, we propose a unified modeling strategy for multi-frequency PPP AR based on raw uncombined observations. Based on the unified model, the fractional cycle biases (FCBs) generated from multi-frequency observations can be flexibly used, such as for dual- or triple- frequency PPP AR. Its efficiency is verified with Galileo and BeiDou triple-frequency observations collected from globally distributed MGEX stations. The estimated FCB are assessed with respect to residual distributions and standard deviations. The obtained results indicate good consistency between the input float ambiguities and the generated FCBs. To assess the performance of the triple-frequency PPP AR, 11 days of MGEX data are processed in three-hour sessions. The positional biases in the ambiguity-fixed solutions are significantly reduced compared with the float solutions. The improvements are 49.2%, 38.3%, and 29.6%, respectively, in east/north/up components for positioning with BDS, while the corresponding improvements are 60.0%, 29.0%, and 21.1% for positioning with Galileo. These results confirm the efficiency of the proposed approach, and that the triple-frequency PPP AR can bring an obvious benefit to the ambiguity-float PPP solution

Undifferenced and Uncombined GNSS Time Transfer and its Space Applications

This thesis presents a framework for developing a state-of-the-art undifferenced and uncombined (UDUC) time transfer technique for space applications. It addresses challenges in GNSS time transfer, such as multi-frequency signal modelling, satellite clock estimation, and hardware delay variations. The thesis introduces the UDUC POD method for GNSS time transfer in space and explores the feasibility of constructing a LEO-based space-time reference. This PhD dissertation is among the first to investigate the UDUC GNSS time transfer