Global Navigation Satellite Systems

During the past few decades, the presence of global navigation satellite systems (GNSSs) such as GPS, GLONASS, Beidou and Galileo has facilitated positioning, navigation and timing (PNT) for various outdoor applications. With the rapid increase in the number of orbiting satellites per GNSS, enhancements in the satellite-based augmentation systems (SBASs) such as EGNOS and WAAS, as well as commissioning new GNSS constellations, the PNT capabilities are maximized to reach new frontiers. Additionally, the recent developments in precise point positioning (PPP) and real time kinematic (RTK) algorithms have provided more feasibility to carrier-phase precision positioning solutions up to the third-dimensional localization. With the rapid growth of internet of things (IoT) applications, seamless navigation becomes very crucial for numerous PNT dependent applications especially in sensitive fields such as safety and industrial applications. Throughout the years, GNSSs have maintained sufficiently acceptable performance in PNT, in RTK and PPP applications however GNSS experienced major challenges in some complicated signal environments. In many scenarios, GNSS signal suffers deterioration due to multipath fading and attenuation in densely obscured environments that comprise stout obstructions. Recently, there has been a growing demand e.g. in the autonomous-things domain in adopting reliable systems that accurately estimate position, velocity and time (PVT) observables. Such demand in many applications also facilitates the retrieval of information about the six degrees of freedom (6-DOF - x, y, z, roll, pitch, and heading) movements of the target anchors. Numerous modern applications are regarded as beneficiaries of precise PNT solutions such as the unmanned aerial vehicles (UAV), the automatic guided vehicles (AGV) and the intelligent transportation system (ITS). Hence, multi-sensor fusion technology has become very vital in seamless navigation systems owing to its complementary capabilities to GNSSs. Fusion-based positioning in multi-sensor technology comprises the use of multiple sensors measurements for further refinement in addition to the primary GNSS, which results in high precision and less erroneous localization. Inertial navigation systems (INSs) and their inertial measurement units (IMUs) are the most commonly used technologies for augmenting GNSS in multi-sensor integrated systems. In this article, we survey the most recent literature on multi-sensor GNSS technology for seamless navigation. We provide an overall perspective for the advantages, the challenges and the recent developments of the fusion-based GNSS navigation realm as well as analyze the gap between scientific advances and commercial offerings. INS/GNSS and IMU/GNSS systems have proven to be very reliable in GNSS-denied environments where satellite signal degradation is at its peak, that is why both integrated systems are very abundant in the relevant literature. In addition, the light detection and ranging (LiDAR) systems are widely adopted in the literature for its capability to provide 6-DOF to mobile vehicles and autonomous robots. LiDARs are very accurate systems however they are not suitable for low-cost positioning due to the expensive initial costs. Moreover, several other techniques from the radio frequency (RF) spectrum are utilized as multi-sensor systems such as cellular networks, WiFi, ultra-wideband (UWB) and Bluetooth. The cellular-based systems are very suitable for outdoor navigation applications while WiFi-based, UWB-based and Bluetooth-based systems are efficient in indoor positioning systems (IPS). However, to achieve reliable PVT estimations in multi-sensor GNSS navigation, optimal algorithms should be developed to mitigate the estimation errors resulting from non-line-of-sight (NLOS) GNSS situations. Examples of the most commonly used algorithms for trilateration-based positioning are Kalman filters, weighted least square (WLS), particle filters (PF) and many other hybrid algorithms by mixing one or more algorithms together. In this paper, the reviewed articles under study and comparison are presented by highlighting their motivation, the methodology of implementation, the modelling utilized and the performed experiments. Then they are assessed with respect to the published results focusing on achieved accuracy, robustness and overall implementation cost-benefits as performance metrics. Our summarizing survey assesses the most promising, highly ranked and recent articles that comprise insights into the future of GNSS technology with multi-sensor fusion technique.

Ship navigation requires accurate positioning, navigation and timing (PNT) data. PNT data from a single source has uncertainties and potential risks. Wrong PNT data has a huge impact on ship maneuvering, and at the same time, it may cause huge losses to national assets and national security. This chapter proposes a data fusion algorithm based on single-frequency global navigation satellite system (GNSS) and inertial navigation system (INS) to obtain PNT data, which can improve the availability, accuracy, reliability, continuity, and robustness. The experimental results show that the data fusion method combining median filtering and Kalman filtering can improve the system's ability to acquire PNT data. When blocking GNSS acquisition of PNT data, relying on INS, you can still obtain PNT data, which can make up for the ability to obtain PNT data from GNSS.

The advent of Global Navigation Satellite Systems (GNSS) initiated a revolution in Positioning, Navigation and Timing (PNT) applications. Besides the enormous impact on geospatial data acquisition and reality capture, satellite navigation has penetrated everyday life, a fact which is proved by the increasing degree of human reliance on GNSS-enabled smart devices to perform casual activities. Nevertheless, GNSS does not perform well in all cases. Specifically, in GNSS-challenging environments, such as urban canyons or forested areas, navigation performance may be significantly degraded or even nullified. Consequently, positioning is achieved by combining GNSS with additional heterogeneous information or sensors, such as inertial sensors. To date, most smartphones are equipped with at least accelerometers and gyroscopes, besides GNSS chipsets. In the frame of this research, difficult localization scenarios were investigated to assess the performance of these low-cost inertial sensors with respect to higher grade GNSS and IMU systems. Four state-of-the-art smartphones were mounted on a specifically designed on-purpose build platform along with reference equipment. The platform was installed on top of a vehicle, which was driven by a predefined trajectory that included several GNSS-challenging parts. Consequently, positioning and inertial readings were acquired by smartphones and compared to the information collected by the reference equipment. The results indicated that although the smartphone GNSS receivers have increased sensitivity, they were unable to produce an acceptable solution for more than 30% of the driven course. However, all smartphones managed to identify, up to a satisfactory degree, distinct driving features, such as curves or bumps.

The Russian Global Navigation Satellite System (GLONASS) satellites have a stretched body shape and take a specific attitude mode inside the eclipse. Based on previous studies, the new Empirical CODE orbit model (ECOM2) performs better than the classical ECOM model if a satellite has elongated shape or does not maintain yaw-steering mode, and the use of an a priori box-wing (BW) model improves the orbits significantly when employing the ECOM model. However, we find that the ECOM model performs better than the ECOM2 model for GLONASS satellites outside eclipse seasons, while it performs two times worse in eclipse seasons. The use of the conventional box-wing model results in very little improvement. By assessing the ECOM Y _{0} estimates, we conclude that there are potential radiators on the -x surface of GLONASS satellites causing orbit perturbations also inside the eclipse. The higher-order Fourier terms of the ECOM2 model can compensate for such effects better than the ECOM model. Based on this finding, we first confirm that GLONASS-K satellites take a similar attitude mode as GLONASS-M satellites inside the eclipse. Then, we adjust optical parameters of GLONASS satellites as part of precise orbit determination (POD) considering the potential radiator and thermal radiation effects. Finally, the adjusted parameters are introduced into a new box-wing model and jointly used with the ECOM and ECOM2 model, respectively. Results show that the amplitude and the dependency of the empirical parameters on the beta angle are greatly reduced for both ECOM and ECOM2 models. Rather than the conventional box-wing model, the new box-wing model reduces the orbit misclosure between two consecutive arcs for both GLONASS-M and GLONASS-K satellites. In particular, the improvement in GLONASS-M satellites is more than 30% for the ECOM model during eclipse seasons. Further evaluation from 24-h predicted orbits demonstrates that the improvement during eclipse seasons is mainly in along- and cross-track directions. Finally, we validate GLONASS satellite orbits using Satellite Laser Ranging (SLR) observations. The use of the new box-wing model reduces the spurious pattern of the SLR residuals as a function of beta and Delta u significantly, and the linear dependency of the SLR residuals on the elongation drops from as large as -0.760 mm/deg to almost zero for both ECOM and ECOM2 models. In general, GLONASS-M satellites benefit more from the new a priori box-wing model and the BW+ECOM model results in the best SLR residuals, with an improvement of about 50% and 20%, respectively, for the mean and standard deviation (STD) values with respect to the orbit products without a priori model.

Since the beginning of the International Global Navigation Satellite System (GLONASS) Experiment, IGEX, in October 1998, the Center for Orbit Determination in Europe (CODE) has acted as an analysis center providing precise GLONASS orbits on a regular basis. In CODE's IGEX routine analysis the Global Positioning System (GPS) orbits and Earth rotation parameters are introduced as known quantities into the GLONASS processing. A new approach is studied, where data from the IGEX network are combined with GPS observations from the International GPS Service (IGS) network and all parameters (GPS and GLONASS orbits, Earth rotation parameters, and site coordinates) are estimated in one processing step. The influence of different solar radiation pressure parameterizations on the GLONASS orbits is studied using different parameter subsets of the extended CODE orbit model. Parameterization with three constant terms in the three orthogonal directions, D, Y, and X (D = direction satellite–Sun, Y = direction of the satellite's solar panel axis), and two periodic terms in the X-direction, proves to be adequate for GLONASS satellites. As a result of the processing it is found that the solar radiation pressure effect for the GLONASS satellites is significantly different in the Y-direction from that for the GPS satellites, and an extensive analysis is carried out to investigate the effect in detail. SLR observations from the ILRS network are used as an independent check on the quality of the GLONASS orbital solutions. Both processing aspects, combining the two networks and changing the orbit parameterization, significantly improve the quality of the determined GLONASS orbits compared to the orbits stemming from CODE's IGEX routine processing.

To achieve an accurate and robust localization result under Global Navigation Satellite System (GNSS) denied environment, coupling GNSS with other sensors tends to be a promising solution, also known as the sensor fusion system. One of the most challenging scenarios in sensor fusion is to couple vision navigation system with GNSS, especially the tightly-coupled fusion. Compared with those loosely coupled ones, it performs the information fusion on a more intrinsic level, which can benefit the accuracy and robustness of the system. However, the major barrier lying in front of tightly coupling method is the way to unify the heterogeneous and asynchronous GNSS observations with vision observations. To solve this problem, we start from the Vison Simultaneous Localization and Mapping (V-SLAM) method, which has already become the mainstream in vision navigation domain. By utilizing existing attempts on graph-optimization-based GNSS data processing, we propose a joint graph optimization combining vision-SLAM with GNSS pseudorange. It is a tightly coupled fusion of GNSS and stereo camera, implemented by considering GNSS satellites as vision feature points. Based on the framework of mature state-of-art pose-graph SLAM, a graph optimization is applied simultaneously on GNSS and vision positioning, to achieve a robust, accurate, yet versatile fusion system.

Aiming at the Terrestrial Water Storage(TWS) changes in the Amazon River basin, this article uses the coordinate time series data of the Global Navigation Satellite System (GNSS), adopts the Variational Mode Decomposition and Bidirectional Long and Short Term Memory(VMD-BiLSTM) method to extract the vertical crustal deformation series, and then adopts the Principal Component Analysis(PCA) method to invert the changes of terrestrial water storage in the Amazon Basin from July 15, 2012 to July 25, 2018. Then, the GNSS inversion results were compared with the equivalent water height retrieved from Gravity Recovery and Climate Experiment (GRACE) data. The results show that (1) the extraction method proposed in this article has better denoising effect than the traditional method; (2) the surface hydrological load deformation can be well calculated using GNSS coordinate vertical time series, and then the regional TWS changes can be inverted, which has a good consistency with the result of GRACE inversion of water storage, and has almost the same seasonal variation characteristics; (3) There is a strong correlation between TWS changes retrieved by GNSS based on surface deformation characteristics and water mass changes calculated by GRACE based on gravitational field changes, but GNSS satellite’s all-weather measurement results in a finer time scale compared with GRACE inversion results. In summary, GNSS can be used as a supplementary technology for monitoring terrestrial water storage changes, and can complement the advantages of GRACE technology.

Positioning, Navigation and Timing (PNT) services based on the Global Navigation Satellite System (GNSS) have a number of known drawbacks that makes them prone to suffer a disruption. These flaws can be exploited to disable this service in critical infrastructures. Therefore, the hybridization of GNSS and 5G measurements enabling Assured PNT (APNT) services is growing up and gaining attention by governmental bodies. 5G signals emitted by terrestrial networks and by Low Earth Orbit (LEO) satellites can complement GNSS, enhancing their reliability, security and accuracy. For example, the larger Signal-to-Noise Ratio (SNR) of a terrestrial system makes them more difficult to suffer a jamming attack as compared to GNSS signals. A joint data and positioning signal enhances the security and reliability of the APNT solution with, for example, the successful decoding of encrypted data, this could be used for system authentication. Moreover, a larger signal bandwidth increases the accuracy of the PNT solution. All of these benefits can be extracted from the 5G terrestrial and non-terrestrial networks that transmit the Positioning Reference Signal (PRS). The rationale for these benefits rely on the flexibility of this pilot signal in terms of bandwidth or symbols used for the APNT service. This paper aims to analyse how this flexibility can be optimized to achieve a specific level of accuracy, minimizing the resources used under certain constrains of bandwidth or latency. The results present the optimal parameters to design a 5G PRS waveform with a target scenario constrains. Then, we compare it with the GNSS service and how to benefit when using them in combination. Finally, the paper concludes with the future research lines proposing a joint communications and positioning system as a closed loop. In such scenario, the user achieved accuracy is sent back to the infrastructure, and using the dynamic PRS flexibility, the system increases or reduces the PRS resources in order to meet the user accuracy requirements.

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

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.

The landscape of Global Navigation Satellite Systems (GNSS) signals is undergoing significant change. The parallel development of new, more robust and versatile signals along with two new GNSS systems with global reach is having a dramatic impact on the development of GNSS receivers. Receiver manufacturers are continually developing and implementing unique signal acquisition and tracking algorithms, advanced integrity monitoring algorithms, advanced multipath mitigation algorithms and a host of other enhancements in an effort to improve the performance of GNSS receivers and make their products stand out in a crowded field. The objective of this research is to develop a methodology for comparing the pseudorandom noise (PRN) code tracking performance of various receivers. While the overall performance of a receiver depends on many more factors, effective PRN code tracking is fundamental to the operation of a GNSS receiver. The methodology developed in this research requires three fundamental measurements from the receiver under test – code pseudorange, carrier pseudorange and carrier-to-noise-density ratio (C/No). These measurements are generated by all GNSS receivers but may not always be available to the user. Therefore, this methodology can only be performed on receivers that output these values. This methodology might be useful as a tool to evaluate the suitability of various receivers in certain environments or applications (e.g., weak signal environments, high dynamic environments, unfriendly radio frequency (RF) interference environments, etc.) and also as a means of comparing the relative performance of receivers in those environments or applications. Expectations for the research were that the data collection and analysis techniques would generate code loop tracking jitter plots that closely tracked with theoretical curves. In addition, it was expected that different receivers would yield slightly different jitter plots giving them a unique “signature.” Finally, it was expected that some receivers might incorporate scalar code tracking loops and others might incorporate vector tracking loops. The methodology was developed and refined using three receivers from three popular manufacturers, each with a unique set of capabilities. Raw data was collected from each receiver and processed using purpose built algorithms to estimate the code loop tracking jitter for each signal for each receiver. The code loop tracking observations for each of the three receivers tested were compared with theoretical curves generated using closed form equations published in previous papers. In most cases, the data compared favorably. However, there were some significant outliers that are worthy of additional research and analysis. In addition, the jitter plots for one receiver tested indicated that it used scalar code tracking loops and the jitter plots for the other two receivers indicated that they use vector tracking loops. It appears to be possible to determine if a receiver uses scalar tracking loops or vector tracking loops based on the jitter estimates generated in this research. This research indicated that the methodology might be a useful tool for 2 characterizing GNSS receivers and, eventually evaluating their performance in various environments. However, more data collection and analysis is required to further validate and refine the methodology. Also, more analysis of the signals that yielded “non-conforming” jitter plots should be examined in more detail.

A wide-range utilisation of Global Navigation Satellite Systems (GNSS) across technology and socioeconomic domains renders the satellite navigation one of the pillars of the modern civilisation. Tackling and overcoming The inherent shortcomings and emerging threats to provision of robust and resilient GNSS Positioning, Navigation and Timing (PNT) services have become a research subject of the utmost importance. An open access to the GNSS positioning estimation process is fundamental for development of advanced methods for robust and resilient GNSS position estimation. Recent introduction of Google Android 7.0 revision allows for direct access to raw GNSS pseudoranges observed by smartphones. Here we address the opportunities for GNSS positioning estimation improvements, given through exploitation of Google Android Location API, in creation of bespoke GNSS position estimation process in navigation application domain of a GNSS Software-Defined Radio (SDR) receivers in smartphones. We present and validate opportunities for improvement of GNSS position estimation process through utilisation of distributed computing architectures, trusted sources of GNSS augmentation data and utilisation of GNSS positioning methods suitable for targeted classes of GNSS applications Authors are members of European GNSS Agency's GNSS Raw Measurements Task Force, and co-authored the white-paper on GNSS-related Google Android Location API applications.

Position, Navigation and Timing (PNT) is integral to the safety, security and prosperity of the United Kingdom, its Overseas Territories, and wider global interests. PNT is also critical to the UK’s national security supporting command, control, and communications capabilities, and to the cyber enterprise. The loss of PNT services from modalities such as Global Navigation Satellite Systems (GNSS) (like the US Global Positioning System (GPS)) will have a significant impact on our economic wellbeing. This paper describes the development of a framework for the coordination of PNT across the UK Government and for the wider UK. It proposes both a strategic and integrated system-of-system architecture that addresses how to bring the complex area of policy, strategy, skills, legislation, delivery mechanisms and multiple technologies, including research and development, together in a coherent way, integrating existing programmes, as well as proposes how to determine appropriate technology approaches looking to the future. It is the core outcome of an evidence based comprehensive review and PNT Strategy development in the UK with the goal to ensure PNT users have access to resilient and assured PNT services when they need them.

The Gaussian beam for atmospheric refraction (GBAR) method is used to model the effects of the refractive atmosphere between a global navigation satellite system (GNSS) satellite and a low earth orbit (LEO) satellite in a radio occultation (RO) configuration. The wave transmitted by the GNSS satellite is expanded into Gaussian beams using a multibeam expansion procedure (MEP). The wave is modeled in the troposphere by Gaussian beams, and additional MEPs are processed if needed. The refractive atmosphere is modeled by a grid of rectangular cells within each of which is defined a constant and vertical refractivity gradient. The propagation factor at the LEO satellite is used to retrieve the refractivity profile at the perigees. The inverse method is recalled, and the global spherical symmetry assumption is emphasized. The inversion of the simulated propagation factor amplitude data is tested in two simulations. The first one considers a spherically symmetrical refractive atmosphere characterized by a standard exponential refractivity distribution. The second one uses data from weather research and forecast (WRF) simulations in the Caribbean region. The error brought by the spherical symmetry assumption is investigated. The GBAR method provides a new tool for the remote sensing of planetary atmospheres.

Under the high dynamic conditions, Global Navigation Satellite System (GNSS) signals produce great Doppler frequency shifts, which hinders the fast acquisition of signals. Inertial Navigation System (INS)-aided acquisition can improve the acquisition performance, whereas the accuracy of Doppler shift and code phase estimation are mainly determined by the INS precision. The relation between the INS accuracy and Doppler shift estimation error has been derived, while the relation between the INS accuracy and code phase estimation error has not been deduced. In this paper, in order to theoretically analyze the effects of INS errors on the performance of Doppler shift and code phase estimations, the connections between them are re-deduced. Moreover, the curves of the corresponding relations are given for the first time. Then, in order to have a better verification of the INS-aided acquisition, a high dynamic scenario is designed. Furthermore, by using the deduced mathematical relation, the effects of different grade INS on the GNSS (including Global Positioning System (GPS) and BeiDou Navigation Satellite System (BDS)) signal acquisition are analyzed. Experimental results demonstrate that the INS-aided acquisition can reduce the search range of local frequency and code phase, and achieve fast acquisition. According to the experimental results, a suitable INS can be chosen for the deeply coupled integration.