GNSS ground and space segments

The number and scope of Global Navigation Satellite System (GNSS)-based space applications has grown significantly since the first GNSS space receiver was flown in the early 1980's. The vast majority of GNSS space users operate in Low-Earth Orbit (LEO), where the use of GNSS receivers has become routine. However, the use of GNSS has expanded to other orbit regimes like Geostationary Orbits (GEO) and High Eccentric Orbits (HEO) but has been very limited due to the challenges involved. The major challenges for such types of orbits including much weaker signals, reduced geometric diversity, and limited signal availability. In any case, considering the recent development of multiple GNSS constellations and ongoing upgrades to existing constellations, GNSS signal availability will improve significantly. As a result, this expanded multi-GNSS signal capability will enable improved on-orbit navigation performance and will also allow the development of new mission concepts. High altitude space users will especially benefit from this evolution, which will provide GNSS signals to challenging regimes well beyond Low Earth Orbit. These benefits will only be realised, however, if additional signals are designed to be interoperable, are clearly documented and supported. In order to enhance the overall GNSS performance for spacecraft's in regimes from LEO, GEO to HEO and beyond, all Satellite Navigation constellation providers and regional augmentation system providers are working together through the United Nations International Committee on GNSS (ICG) forum to establish an interoperable GNSS Space Service Volume (SSV) for the benefit of all GNSS space users. This paper provides an overview of the technical work and in particular the simulations, performance analysis and discussions of the outcomes and results obtained by the UN ICG Working Group-B in the context of the GNSS Space Service Volume activities, which were supported by all GNSS service providers.

The Kahramanmaraş earthquakes of February 6, 2023 occurred in the tectonically active eastern Mediterranean region near the Kahramanmaraş triple junction, where the Anatolian, Arabian, and African plates converge. This structurally complex boundary is characterized by significant strain accumulation, which frequently generates large-magnitude earthquakes. The significant ground shaking experienced in southeastern Turkey and surrounding areas implies the importance of high-resolution studies aimed at monitoring detailed seismotectonic processes in this region.Here, we present a kinematic rupture model of these destructive events derived from a joint inversion of high-rate Global Navigation Satellite System (GNSS) and strong ground motion (SGM) data. We combine GNSS-derived displacement time series from continuously operating stations with displacement waveforms extracted from SGM records. We assess the correlation between these two datasets by comparing stations in close proximity, thereby evaluating the consistency and precision of the derived ground motions. In addition, the earthquake hypocenter and the surface fault trace are taken into account by classifying and grouping the data based on source-to-station geometry. We employ different filtering approaches for near-field and far-field observations to extract the displacements appropriately. We also use the Precise Point Positioning (PPP) technique to obtain the coseismic displacement of GNSS observations. Instead of traditional differential techniques, we tested kinematic PPP while still preserving the effect of ambiguity resolution. Kinematic PPP solutions are derived from raw GNSS phase and pseudorange observations, yielding precise station positions at each epoch during each earthquake separately.To address the spatiotemporal evolution of fault slip, we apply finite fault modeling to the combined dataset and obtain a detailed kinematic rupture model for the earthquake sequence. We compare this model with previously published rupture models for the February 6, 2023 Kahramanmaraş earthquakes, highlighting both similarities in overall fault geometry and slip patterns, and differences in rupture extent and timing. By integrating high-frequency strong ground motion observations with GNSS displacements, we emphasize the importance of combining multiple data sources to gain a more comprehensive understanding of earthquake source processes.

Implementing the International GNSS Service 2021+ Strategic Plan: Vision and Use Cases

Urban environments present major challenges to the global navigation satellite system (GNSS) positioning to estimate the position of a self-driving vehicle. GNSS signals are often obstructed by buildings and other obstacles, leading to reflected and diffracted signals, which cause major positioning errors and known as line-of-sight (LOS) and non-line-of-sight (NLOS) multipath errors. Particularly, real-time kinematic (RTK)-GNSS, which can estimate the position in the centimeter-level accuracy, cannot estimate the precise position when there exist NLOS multipath errors. If we use NLOS GNSS code and carrier phase multipath measurements into the RTK-GNSS algorithm, carrier phase integer ambiguity cannot be solved, or wrong integer ambiguities are estimated. As a result, integer ambiguity resolved to fix solutions cannot be estimated, or wrong fix solutions which have large position error are estimated. That is a major barrier to using RTK-GNSS in autonomous driving. In this paper, we propose a novel NLOS detection and exclusion technique to enhance existing RTK-GNSS positioning in urban areas using only GNSS measurements alone. The key idea behind the proposed technique is that the NLOS pseudorange error will always be a positive value. NLOS pseudorange is longer than the true range, which is the distance between the GNSS antenna and satellite, due to the time delay caused by reflection and diffraction. The distribution of the NLOS error is not along with a Gaussian distribution. We use this phenomenon to detect the NLOS signals. We propose a technique to detect and eliminate the NLOS multipath using double-differenced pseudorange residuals (DD-PR). In the RTK-GNSS, DD GNSS measurement between the rover and base station is typically used. Here, we compute the DD-PR to detect the NLOS signals. In order to compute the DD-PR, the true position of the vehicle must be determined in advance. To solve this contradiction, we use a particle filter. The particle filter generates the position hypotheses, which is called as “particle”. These particles are used as the position candidate for estimating the DD-PR. When the DD-PR can be calculated at each particle position, we detect the large positive errors in DD-PR as NLOS signals. Then we compute the integer ambiguity of only the LOS DD carrier phase to estimate the precise vehicle position. The flow of the proposed technique is as follows: (1) The hypothetical user positions are created as particles, where the state vectors of these particles are 3D positions, x, y, and z. The initial particles are distributed based on the solution of the normal GNSS point positioning and its variance. (2) In the prediction step of the particle filter, the particle state in the next time step is estimated using the velocity of each particle computed from GNSS Doppler measurements. (3) Next, we compute the DD-PR at each particle location. The receiver clock bias, satellite clock bias, ionosphere error, and troposphere error in GNSS pseudorange are canceled through DD computation. As a result, DD geometrical range between receiver and satellite and DD multipath errors remain in the DD measurement. We compute DD-PR using the particle position. As a result, the only multipath error remains if the particle is close to the true position. NLOS multipath error is always positive value; we generate the subset of the LOS measurement hypothesis at each particle using a simple threshold test. (4) Next, we compute the RTK-GNSS position solution using the generated subset of the LOS measurement hypothesis at each particle location. It is expected that the RTK-GNSS fix solution can be calculated if the particle location is close to the true position because the NLOS signal has been eliminated. By contrast, if the particle is far from true position, the integer ambiguity cannot be solved because of using the incorrect subset of LOS measurements. The likelihood of each particle is computed from a distance between the particle location and the RTK-GNSS fix solution. The particle that is closest to the true position has a higher likelihood than the other particles. The probabilistic weight of each particle is then updated based on its likelihood. (5) Finally, all the particles are resampled based on their new weights. Particles that are sufficiently close to the true position survive this resampling step, and the average position of the remaining particles are employed as the final estimated user position. To confirm the effectiveness of the proposed technique, a kinematic positioning test was performed in Shinjuku, Tokyo, Japan. From the several positioning tests, the proposed technique could dramatically improve the availability and accuracy of RTK-GNSS fix solutions in the dense urban canyon where there exist a lot of NLOS multipath signals.

A Global Navigation Satellite System (GNSS) enables continuous positioning anywhere on or near the Earth surface by means of radio signals transmitted from orbiting satellites. The system comprises a constellation of about 20 or more satellites, a network of ground stations for monitoring and control of the satellites, and an unlimited number of users who passively receive the signals and employ them for positioning and other applications. GNSS satellites transmit carrier signals in the range of 1.1–1.8 GHz, which are modulated by digital codes, termed pseudo‐random noise (PRN) codes. The codes are selected so as to enable all satellites in the constellation to transmit at the same frequency with minimal interference, and to permit range measurements to be made. Given information on the satellite locations and measurements of range to at least four satellites, a receiver on or near the surface of the Earth can solve for its three dimensional position and time. GNSS is widely used for navigation and positioning of aircraft and satellites. Aviation users in particular, have very stringent requirements that are driving current augmentation and modernization efforts.

Global Navigation Satellite System (GNSS) is vulnerable to spoofing attack because of its low power and open civil signal structure. Loran-C system is independent of GNSS and can be used for anti-spoofing. In order to detect, identify the spoofing signals and exclude its influence on position, velocity, and time (PVT) results, the paper proposes the GNSS anti-spoofing method assisted by Loran-C system. When the number of Loran-C signals is relatively less, the difference observations of Loran-C are used to identify whether the GNSS positioning result is right or wrong, and the factors affecting the detection performance are studied. When the Loran-C receiver can provide independent positioning information, all spoofing signals are identified by the clustering algorithm after the consistency detection of GNSS pseudorange observations, Loran-C positioning information and satellite positions. Then if the number of remained GNSS signals after spoofing exclusion is less than 4, the GNSS signals and Loran-C signals are combined to improve the positioning accuracy and the Dilution of Precision (DOP) is used to measure the positioning accuracy. Finally, the feasibility of the proposed method is verified by simulation experiments.

When compared to the wide range of atmospheric sensing techniques, global navigation satellite system (GNSS) offers the advantage of operating under all weather conditions, is continuous, with high temporal and spatial resolution and high accuracy, and has long-term stability. The utilisation of GNSS ground networks of continuous stations for operational weather and climate services is already in place in many nations in Europe, Asia, and America under different initiatives and organisations. In Africa, the situation appears to be different. The focus of this paper is to assess the conditions of the existing and anticipated GNSS reference network in the African region for meteorological applications. The technical issues related to the implementation of near-real-time (NRT) GNSS meteorology are also discussed, including the data and network requirements for meteorological and climate applications. We conclude from this study that the African GNSS network is sparse in the north and central regions of the continent, with a dense network in the south and fairly dense network in the west and east regions of the continent. Most stations lack collocated meteorological sensors and other geodetic observing systems as called for by the GCOS Reference Upper Air Network (GRUAN) GNSS Precipitable Water Task Team and the World Meteorological Organization (WMO). Preliminary results of calculated zenith tropospheric delay (ZTD) from the African GNSS indicate spatial variability and diurnal dependence of ZTD. To improve the density and geometry of the existing network, countries are urged to contribute more stations to the African Geodetic Reference Frame (AFREF) program and a collaborative scheme between different organisations maintaining different GNSS stations on the continent is recommended. The benefit of using spaced based GNSS radio occultation (RO) data for atmospheric sounding is highlighted and filling of geographical gaps from the station-based observation network with GNSS RO is also proposed.

Global navigation satellite system (GNSS) plays a crucial role in providing the globally referenced positioning for self-driving systems. Unfortunately, the numerous multipath or non-line-of-sight (NLOS) receptions (known as outlier observations) caused by the signal reflections from buildings reduce the positioning accuracy of GNSS in dense urban environments. The recently investigated factor graph- based GNSS positioning formulation simultaneously considers the historical information, which significantly increases the measurement redundancy of state estimation. Taking this advantage, this paper proposes an outlier mitigation method where the bias involved in the outliers is estimated simultaneously with the position of the receiver. Specifically, the outliers are firstly detected using a pre-trained deep learning network. Secondly, an unknown variable associated with the bias is assigned to each identified outlier measurement. Then the position of the GNSS receiver, together with the bias of outlier measurements, is estimated simultaneously via the factor graph optimization (FGO) based on the pseudorange measurements and Doppler frequency shift. Finally, the effectiveness of the proposed method is validated using a dataset collected in the urban canyon by a low-cost automobile- level GNSS receiver.

The Global Navigation Satellite System (GNSS) provides a continuous, high precision, global, real-time, and all weather navigation and positioning, which powerfully contributes to all scientific questions related to precise positioning on Earth’s surface. Recently, the reflected and refracted signals from GNSS satellites resulted in many new applications in environmental remote sensing. The GNSS refracted signals from Radio Occultation satellites together with ground GNSS observations have produced wide applications in atmospheric remote sensing, including global monitoring of tropospheric water vapour, temperature and pressure, tropopause parameters and ionospheric parameters as well as ionospheric irregularities. The GNSS reflected signals from the ocean and land surface could determine the wave height, wind speed and wind direction of ocean surface and land surface conditions. In this paper, GNSS remote sensing applications in the atmosphere, oceans, land and hydrology are presented as well as recent results. With more and more GNSS satellite constellations in coming years, it is expected more and wider applications in various environment remote sensing.

In urban areas, signal reception conditions are often poor due to reflections from buildings, resulting in inaccurate global navigation satellite system (GNSS)-based positioning. Various 3D-mapping-aided (3DMA) GNSS techniques, including shadow matching, have been proposed to address this issue. However, conventional shadow matching estimates positions in a discretized manner. The accuracy of this approach is limited by the resolution of the grid points representing the candidate receiver positions, making it difficult to achieve robust urban positioning and to ensure that the position estimate satisfies user-specified protection levels or safety bounds. To overcome these limitations, zonotope shadow matching (ZSM) has been proposed, which utilizes a set-based position estimate rather than grid-based estimates. ZSM calculates the GNSS shadow—an area on the ground where the line-of-sight (LOS) is blocked and only non-line-of-sight (NLOS) signals can be received—to estimate the receiver’s position set. ZSM distinguishes between LOS and NLOS satellites, determining that the receiver is inside the GNSS shadow if the satellite is NLOS and outside if the satellite is LOS. However, relying solely on GNSS shadows limits the ability to sufficiently reduce the size of the receiver position set and to precisely estimate the receiver’s location. To address this, we propose zonotope shadow and reflection matching (ZSRM) to enhance positioning accuracy in urban areas. ZSRM incorporates both GNSS shadows and GNSS reflections to estimate the receiver’s position. A GNSS reflection is defined as an area on the ground where both LOS and NLOS signals are received simultaneously. Unlike ZSM, ZSRM classifies satellites into three types: LOS-only, LOS + NLOS, and NLOS-only. Based on this classification, the receiver’s position is determined in one of three ways: inside the GNSS shadow (if NLOS-only), inside the GNSS reflection (if LOS + NLOS), or outside both areas (if LOS-only). Using this framework, ZSRM can estimate the receiver’s position more precisely than ZSM. The proposed ZSRM technique is validated through field tests using GNSS signals collected in an urban environment. Consequently, the RMS horizontal position error of ZSRM improved by 10.0% to 53.6% compared with ZSM, while the RMS cross-street and along-street position bounds improved by 18.0% to 50.1% and 30.7% to 59.3%, respectively.

This paper proposes the development of Ground Control System that integrates ground control points (GCP) markers with global navigation satellite system (GNSS) mini-receivers for UAV-based mapping. Conventional UAV survey methods require specialized high cost equipment and significant human effort. In contrast, the system proposed here uses low-cost GNSS devices, and small board computers to store raw data from the receivers. The software used to perform geometric adjustment of ground control points is open source and the cost of building ten panels is less than the cost of a single commercial off-the-shelf (COTS) high-precision GNSS survey equipment. The main components of the developed system are: (i) hardware components; (ii) software components; (iii) data collection workflow, (iv) accuracy assessment. Performance of the proposed system was tested in two experimental UAV flight missions over potato crop fields. Results show that the developed system reduces both the information capture time and the fieldwork effort in applications such as UAV-based mapping. Furthermore, accuracy assessment on digital elevation and orthophotomosaic models show that positional accuracy is acceptable for precision farming purposes

<p>Knowledge of the ionospheric electron density is essential for a wide range of applications, e.g., telecommunications, satellite positioning and navigation, and Earth observation from space. Therefore, considerable efforts have been concentrated on modeling this ionospheric parameter of interest. Ionospheric electron density is characterized by high complexity and is space−and time−varying, as it is highly dependent on local time, latitude, longitude, season, solar cycle and activity, and geomagnetic conditions. Daytime disturbances cause periodic changes in total electron content (diurnal variation) and additionally, there are multi-day periodicities, seasonal variations, latitudinal variations, or even ionospheric perturbations that cause fluctuations in signal transmission.</p><p>Because of its multiple band frequencies, the current Global Navigation Satellite Systems (GNSS) offer an excellent example of how we can infer ionosphere conditions from its effect on the radiosignals from different GNSS band frequencies. Thus, GNSS techniques provide a way of directly measuring the electron density in the ionosphere. The main advantage of such techniques is the provision of the integrated electron content measurements along the satellite-to-receiver line-of-sight at a large number of sites over a large geographic area.</p><p>Deep learning techniques are essential to reveal accurate ionospheric conditions and create representations at high levels of abstraction. These methods can successfully deal with non-linearity and complexity and are capable of identifying complex data patterns, achieving accurate ionosphere modeling. One application that has recently attracted considerable attention within the geodetic community is the possibility of applying these techniques in order to model the ionosphere delays based on GNSS satellite signals.</p><p>This paper deals with a modeling approach suitable for predicting the ionosphere delay at different locations of the IGS network stations using an adaptive Convolutional Neural Network (CNN). As experimental data we used actual GNSS observations from selected stations of the global IGS network which were participating in the still-ongoing MGEX project that provides various satellite signals from the currently available multiple navigation satellite systems. Slant TEC data (STEC) were obtained using the undifferenced and unconstrained PPP technique. The STEC data were provided by GAMP software and converted to VTEC data values. The proposed CNN uses the following basic information: GNSS signal azimuth and elevation angle, GNSS satellite position (x and y). Then, the adaptive CNN utilizes these data inputs along with the predicted VTEC values of the first CNN for the previous observation epochs. Topics to be discussed in the paper include the design of the CNN network structure, training strategy, data analysis, as well as preliminary testing results of the ionospheric delays predictions as compared with the IGS ionosphere products.   </p>

Many sophisticated Global Navigation Satellite System (GNSS ) applications require high-precision satellite orbit and clock products. The GNSS orbits and clocks are usually derived from the analysis of tracking data collected by a globally distributed GNSS receiver network. The estimation process adjusts parameters for the satellite orbits, transmitter and receiver clocks, station positions, tropospheric delays, Earth orientation, intersystem and interfrequency biases, and carrier-phase ambiguities. The estimation requires detailed modeling of geophysical processes, atmospheric and relativistic effects, receiver tracking modes, antenna phase centers, spacecraft properties, and attitude control algorithms. This chapter describes precise orbit and clock determination of the GNSS constellations as performed by the analysis centers of the International GNSS Service, including models, estimation strategies, products, and the combination of orbit and clock solutions.

Time synchronization is one of the main applications of Global Navigation Satellite System (GNSS). Indeed, GNSS satellites are equipped with highly stable atomic clocks, which can be used as clock references. In particular, Galileo constellation satellites are equipped with Rubidium and Hydrogen-maser frequency standards that have a long-term (1-day) stability at the level of 10-14 for intervals of 104 s. However, in order to use these clocks as references, their time offsets must be estimated from measurements collected on ground. In this sense, the effectiveness of these references depends on the accuracy and stability of such estimates. For that reason, the International GNSS Service (IGS) is providing satellite solutions with a nominal accuracy of 75 ps (i.e. better than 3 cm). The clock accuracy is assessed by evaluating the discontinuities over consecutive days, computed independently. These discontinuities appear because of the correlations between the different parameters, which are estimated jointly with the satellite clocks (e.g. receiver clocks or carrier phase ambiguities). Therefore, satellite clocks depend on the robustness of the different models used in the estimation process. For the Galileo satellites, the inter-day discontinuities are at the level of 0.5 ns. These large jumps are due to the minor number of Galileo satellites available than in GPS, which results in a reduced number of fixed carrier phase ambiguities (other solutions from other IGS analysis centers present similar features). In this work we present a refinement of the method for estimating satellite and receiver clocks, which is able to reduce these discontinuities and, consequently, to improve the accuracy of the satellite clock estimations (according to the above-mentioned IGS metric applied). Some of the main characteristics of this new approach are 1) the use of unambiguous carrier phases, 2) processing several constellations and several frequencies and 3) considering the temperature dependency of the phase biases.

Global Navigation Satellite System (GNSS) Radio Occultation measurements can remote sensing Earth's neutral atmosphere and ionosphere. Meanwhile, another GNSS remote sensing technique named GNSS Reflectometry (GNSS-R) has been developed rapidly, which can retrieve sea surface wind speed. The GNSS Occultation Sounder (GNOS) II instrument has been designed to integrate these two GNSS remote sensing techniques in one payload of the third batch FengYun-3 (FY-3) satellites. This paper presents the advancements in research of GNOS II and instrument performance. The qualified module of GNOS II has been manufactured by National Space Science Center (NSSC), Chinese Academy of Sciences. We show that the phase center variety is low enough for the new designed precise orbit determination (POD) antenna to increase the accuracy of precise orbit determination. A wide coverage atmosphere occultation antenna and a 65nm technics ASIC are used in the new generation GNOS instrument. As an important improvement, the GNOS II can generate Delay Doppler Maps (DDMs) of GNSS signals scattered from the ocean surface. The result of test shows that the GNOS II instrument has high performance in precision of code, carrier phase and real-time navigation.