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Monocular vision sensors are often affected by the rapid direction change in load platform and violent illumination change when the mobile device moves autonomously with high maneuverability. The images collected by the visual sensor will also have a lot of dynamic blur, which together with the weak texture environment reduces the continuity and accuracy of the visual autonomous navigation system. To enhance the stability of the system, in the letter, we propose a vision-led multisource data fusion navigation algorithm. The system combines the visual information for trajectory estimation, adds the inertial measurement unit (IMU) measurement information to the sliding window for optimization, and finally uses the global navigation satellite system (GNSS) data as a reconstraint condition through factor graph optimization to further optimize the trajectory accuracy. Experiments on the public datasets containing a variety of different scene categories show that the trajectory tracking results generated by our algorithm are more complete and stable and can better meet the system’s autonomous navigation requirements. [ABSTRACT FROM AUTHOR]

Efficient use of global navigation satellite system (GNSS) observations improves when applying rational satellite selection algorithms. By combining the Sherman–Morrison formula and singular value decomposition, a smaller-GDOP (geometric dilution of precision)-value method is proven for an increasing number of visible satellites. By combining this smaller-GDOP-value method with the maximum-volume-tetrahedron method, a new rapid satellite selection algorithm based on the Sherman–Morrison formula for GNSS multi-systems is proposed. The basic idea of the algorithm is as follows: First, the maximum-volume-tetrahedron method is used to obtain four initial visible satellites. Then, the other visible satellites are selected by using the smaller-GDOP-value method to reduce the GDOP value and improve the accuracy of the overall algorithm. When the number of included satellites reaches a certain value, the rate of GDOP decrease tends to approach zero. Considering the algorithm precision and the computation efficiency, reasonable thresholds and end of calculation condition equation are given, which can make the proposed algorithm autonomous. The reasonable thresholds and the end of calculation parameters are suggested by means of experiments. Under the thresholds and the end of calculation parameters, the algorithm has an adaptive functionality. Furthermore, the GDOP values of the algorithm are less than 2, indicating that this algorithm can meet one of the requirements of high-precision navigation. Moreover, compared with the computation complexity values of the optimal GDOP estimation method, which includes all visible satellites, the values of the new algorithm are about half, indicating that this algorithm has a rapid performance. These findings verify that the proposed satellite selection algorithm based on the Sherman–Morrison formula provides autonomous functionality, high-performance computing, and high-accuracy results. [ABSTRACT FROM AUTHOR]

The use of global navigation satellite systems (GNSS) has been a competitive way to provide high-precision and low-cost time and frequency transfer results. However, the traditional GNSS method, the precise point positioning (PPP), is usually based on the ionosphere-free (IF) combination, which is not flexible when applying multi-frequency scenarios. In addition, PPP relies on precise satellite clock products with an accuracy of tens of picoseconds, limiting the time and frequency transfer performance. More importantly, achieving integer ambiguity resolution (IAR) is challenging, which makes high-precision phase observations underutilized. To achieve a better time transfer performance, we must consider all those factors from the GNSS end. In this contribution, a new GNSS time and frequency model at the undifferenced and uncombined (UDUC) level is first derived. In the UDUC model, the satellite clocks are estimated together with other parameters, and the integer ambiguities are resolved in the double-differenced (DD) form for their reliable estimation. Our numerical tests suggest three major findings. First, with integer ambiguities resolved, the UDUC model with satellite clocks fixed showed a 20% to 50% improvement compared with the UDUC PPP model. Second, with IAR and satellite clocks estimated, the proposed UDUC model shows a 10%–40% improvement over the model with satellite clocks fixed. Third, with integer ambiguities resolved and satellite clocks estimated, GPS, Galileo, and BDS-3 all have the potential to achieve frequency transfer in the low-mid 10 - 17 range for averaging times within one day. [ABSTRACT FROM AUTHOR]

Accurate modeling of zenith tropospheric delay (ZTD) is beneficial for high-precision navigation and positioning. Many models with good performance have been developed for calibrating ZTD, such as the GPT3 model, which is recognized as an excellent global model and is widely used. However, certain limitations still remain in current models, such as the adoption of only single gridded data for modeling, and the model parameters need to be further optimized. In our previous research, a new approach based on the sliding window algorithm was proposed and applied to develop the GZTD-H model to address some of these limitations. However, this model is only suitable for the vertical adjustment of ZTD, not for estimating ZTD directly. In this study, an improved global grid ZTD model considering height scale factor (GGZTD-H) is derived from the initial GZTD-H model for estimating ZTD. The RMSs of the GGZTD-H model are 4.11 cm and 3.29 cm as validated by radiosonde data and IGS data, respectively. Compared with the UNB3m model and the canonical GPT3 model, the new model exhibits better performance. Moreover, three resolutions of the GGZTD-H model have been developed to reduce the quantity of gridded data delivered to users and optimize the ZTD computation process. Compared with the GPT3 model, the GGZTD-H model shows better performance with lower resolution and requires fewer model parameters for ZTD estimation, greatly optimizing ZTD computation. Users may select the best model that meets their needs in terms of the balance between resolution and accuracy. The high-precision GGZTD-H model could be used as a ZTD vertical stratification model for the vertical adjustment of atmospheric data and as an empirical model for ZTD estimation, which has potential applications in GNSS precise positioning, such as for the establishment and maintenance of the global terrestrial reference frame. [ABSTRACT FROM AUTHOR]

Integrity monitoring (IM) is essential if GNSS positioning technologies are to be fully trusted by future intelligent transport systems. A tighter and conservative stochastic model can shrink protection levels in the position domain and therefore enhance the user-level integrity. In this study, the stochastic models for vehicle-based GNSS positioning are refined in three respects: (1) Gaussian bounds of precise orbit and clock error products from the International GNSS Service are used; (2) a variable standard deviation to characterize the residual tropospheric delay after model correction is adopted; and (3) an elevation-dependent model describing the receiver-related errors is adaptively refined using least-squares variance component estimation. The refined stochastic models are used for positioning and IM under the Advanced Receiver Autonomous Integrity Monitoring (ARAIM) framework, which is considered the basis for multi-constellation GNSS navigation to support air navigation in the future. These refinements are assessed via global simulations and real data experiments. Different schemes are designed and tested to evaluate the corresponding enhancements on ARAIM availability for both aviation and ground vehicle-based positioning applications. [ABSTRACT FROM AUTHOR]

Proper signal conditioning is crucial for reliable Global Navigation Satellite System (GNSS) navigation. Signal conditioning includes correcting receiver front-end distortions, shaping noise, removing interferences, and altering the received signal. The high-rate DFT-based data manipulator (HDDM) is a versatile signal processing architecture based on the Discrete Fourier Transform (DFT). In this article, the HDDM is theoretically modeled, analyzed, and evaluated. Some applications are presented, including interference mitigation, spectrum reconstruction, overlay signal design, altering signal modulations, and signal equalization. Additionally, the complexity of one concrete hardware implementation is investigated. The HDDM has shown excellent results with interference mitigation, and it can simultaneously achieve other signal conditioning tasks. Processing several tasks with the same nested architecture provides a significant processing benefit compared to discrete processing architectures. It emphasizes the benefits of a single architecture to simultaneously address several signal condition tasks, as opposed to separate structures requiring significantly more processing or poorer synergy. [ABSTRACT FROM AUTHOR]

Integer ambiguity resolution-enabled precise point positioning (PPP), otherwise known as PPP real-time kinematic (PPP-RTK), recovers the integer nature of ambiguities at a user receiver by delivering the satellite phase biases (SPBs) estimated from a global navigation satellite system (GNSS) network. Due to the rank-deficiency existing between the satellite and receiver phase biases and the ambiguities, the formulation of PPP-RTK model needs to choose a set of unknown parameters as the datum (or the S-basis). Despite the fact that there are non-unique datum choices, one prefers a PPP-RTK model where the estimable SPBs contain a minimum number of datum ambiguities. We will show that otherwise there will be discontinuities occurring in datum ambiguities that will lead to unfavorable jumps in the estimated SPBs and frequent ambiguity resolution (re-)initialization on the user side. For this to occur one normally restricts to a common-view (CV) network, where the satellites are commonly visible to all receivers involved, and constructs the PPP-RTK model by choosing the phase biases and the ambiguities, pertaining to one receiver, as the datum. In doing so the CV model is capable of estimating the SPBs with each bias containing only one datum ambiguity. In this contribution we extend the CV model to an all-in-view (AV) network case where the satellites tracked can differ across receivers, but at least one satellite is commonly visible; this is practical as the network size is normally consisting of baseline lengths of several hundreds of kilometers. Contrary to the CV model, in the AV model the phase biases and the ambiguities pertaining to one satellite is selected as the datum, such that, the number of datum ambiguities entering into the estimable SPBs is always at the minimum as the SPBs are formulated in a between-satellite single-differenced form. The benefits with AV model are that it relieves the stringent satellite visibility as required by the CV model and, at the same time, reduces to the best possible extent any jumps in the estimated SPBs as well as the necessary ambiguity resolution (re-)initialization on the user side. Experiments conducted using multi-GNSS data collected in both CV and AV networks verify that the AV model always outperforms the CV one, as measured by both the time-to-first-fix as well as the positioning accuracy when compared to very precise benchmark coordinates. [ABSTRACT FROM AUTHOR]

Photogrammetric surveys are increasingly being carried out using Unmanned Aerial Vehicles (UAV). Steering drones along the flight profiles is one of the main factors that determines the quality of the compiled photogrammetric products. The aim of this article is to present a methodology for performing and processing measurements, which are used in order to determine the accuracy of steering any drone along flight profiles. The study used a drone equipped with a Global Navigation Satellite System (GNSS) Real Time Kinematic (RTK) receiver. The measurements were performed on two routes which comprised parallel profiles distant from each other by 10 m and 20 m. The study was conducted under favourable meteorological conditions (windless and sunny weather) at three speeds (10 km/h, 20 km/h and 30 km/h). The cross track error (XTE), which is the distance between a UAV's position and the flight profile, calculated transversely to the course, was adopted as the accuracy measure of steering a UAV along the flight profiles. Based on the results obtained, it must be concluded that the values of XTE measures for two representative routes are very similar and are not determined by the flight speed. The XTE68 measure (p = 0.68) ranged from 0.39 m to 1.00 m, while the XTE95 measure (p = 0.95) ranged from 0.60 m to 1.22 m. Moreover, analyses demonstrated that the statistical distribution of the XTE measure was most similar to the gamma and Weibull (3P) distributions. [ABSTRACT FROM AUTHOR]

Sea level rise (SLR) in small island countries such as Kiribati and Tuvalu have been a significant issue for decades. There is an urgent need for more accurate and reliable scientific information regarding SLR and its trend and for more informed decision making. This study uses the tide gauge (TG) dataset obtained from locations in Betio, Kiribati and Funafuti, Tuvalu with sea level corrections for vertical land movement (VLM) at these locations from the data obtained by the Global Navigation Satellite System (GNSS) before the sea level trend and rise predictions. The oceanic feature inputs of water temperature, barometric pressure, wind speed, wind gust, wind direction, air temperature, and three significant lags of sea level are considered in this study for data modeling. A new data decomposition method, namely, successive variational mode decomposition (SVMD), is employed to extract intrinsic modes of each feature that are processed for selection by the Boruta random optimizer (BRO). The study develops a deep learning model, namely, stacked bidirectional long short-term memory (BiLSTM), to make sea level (target variable) predictions that are benchmarked by three other AI models adaptive boosting regressor (AdaBoost), support vector regression (SVR), and multilinear regression (MLR). With a comprehensive evaluation of performance metrics, stacked BiLSTM attains superior results of 0.994207, 0.994079, 0.988219, and 0.899868 for correlation coefficient, Wilmott's Index, the Nash–Sutcliffe Index, and the Legates–McCabe Index, respectively, for Kiribati, and with values of 0.996806, 0.996272, 0.992316, and 0.919732 for correlation coefficient, Wilmott's Index, the Nash–Sutcliffe Index, and the Legates–McCabe Index, respectively, for the case of Tuvalu. It also shows the lowest error metrics in prediction for both study locations. Finally, trend analysis and linear projection are provided with the GNSS-VLM-corrected sea level average for the period 2001 to 2040. The analysis shows an average sea level rate rise of 2.1 mm/yr for Kiribati and 3.9 mm/yr for Tuvalu. It is estimated that Kiribati and Tuvalu will have a rise of 80 mm and 150 mm, respectively, by the year 2040 if estimated from year 2001 with the current trend. [ABSTRACT FROM AUTHOR]

The universal low cost monitoring technique of rural road slopes remains the focus of geotechnical engineering practitioners. Limited by the cost of transport operation and maintenance, the long-term in situ monitoring of mountainous pastoral road slopes is still deficient. In this paper the long term field monitoring of a typical slope in mountainous areas is carried out using the global navigation satellite system (GNSS) and terrestrial laser scanning (TLS) technology. The instability and collapse of the slope are successfully monitored and forewarned. By analysing the temporal and spatial deformation evolution characteristics, the catastrophe mechanism of the slope is revealed. The results show that the results of TLS are in good agreement with GNSS data, and TLS has incomparable advantages for the surface monitoring of dangerous slopes. The geological condition is the internal cause of the landslide, and continuous heavy rainfall is the main trigger factor of the landslide. Loose deposits and sparse vegetation cover on the surface are also important reasons for sliding. This study provides reliable monitoring data for the subsequent reinforcement measures of the slope. Also, it provides a valuable reference for the low cost monitoring and early warning of risk slopes in mountainous areas. [ABSTRACT FROM AUTHOR]

In this article, we propose a coprime array-based technique that enables global navigation satellite system (GNSS) receivers to reliably defend against spoofing attacks with a small time offset. The technique can not only detect spoofing, but also provide the direction of arrival (DOA) of the spoofing source. Our approach is implemented on the raw digital baseband signals, hence it does not need to perform the complex despreading and acquisition stage of the GNSS receiver. Crucially, the detector is still available when the number of signals (including spoofing and satellite signals) is more than the number of array elements. The simulation results demonstrate the effectiveness of the proposed technique in terms of DOA estimation and spoofing detection. [ABSTRACT FROM AUTHOR]

Satellite positioning methods [global navigation satellite system (GNSS) positioning] are used for various applications, which is especially true in the case of the real-time kinematic (RTK) method. This method provides real-time differential data for surveyors to achieve reliable positioning accuracy. In this paper, theoretical foundations for testing the GNSS receivers according to ISO 17123-8 standard are introduced. Using this standard, the positioning performance of a geodetic GNSS receiver Trimble R8s was evaluated using Bosnian Active GNSS Network Bosnia and Herzegovina positioning service (BIHPOS) and a conventional base-rover RTK solution. The main purpose of this study is to evaluate the relationship between the ranging pole support and the quality of the RTK position solution. Besides that, results from RTK positioning using a single base station versus a network of base stations are also compared. Several comprehensive tests were conducted, which involved the most common field procedures where the pole is hand-held by the surveyor and positioned vertically using a circular bubble, whereas in other experiments, a bipod was attached to the range pole, and the GNSS instrument was mounted directly to a tripod via optical plummet-equipped tribrach. The precision, absolute accuracy, and relative accuracy of positioning solutions obtained from these experiments were compared and discussed. The results indicate that regardless of how the RTK range pole was supported, the effect of rover pole holding is within the error of satellite surveys. Furthermore, it was confirmed that using a bipod or a steady hand can produce as accurate RTK results as a tripod. [ABSTRACT FROM AUTHOR]

Harris-Galveston Subsidence District (HGSD), in collaboration with several other agencies, has been operating a dense Global Navigation Satellite System (GNSS) network for subsidence and faulting monitoring within the Greater Houston region since the early 1990s. The GNSS network is designated HoustonNet, comprising approximately 250 permanent GNSS stations as of 2021. This paper documents the methods used to produce position time series, transform coordinates from the global to regional reference frames, identify outliers and steps, analyze seasonal movements, and estimate site velocities and uncertainties. The GNSS positioning methods presented in this paper achieve 2–4-mm RMS accuracy for daily positions in the north–south and east–west directions and 5–8-mm accuracy in the vertical direction within the Greater Houston region. Five-year or longer continuous observations are able to achieve submillimeter-per-year uncertainties (95% confidence interval) for both horizontal and vertical site velocities. Two decades of GNSS observations indicate that Katy in Fort Bend County, Jersey Village in northwestern Harris County, and The Woodlands in southern Montgomery County have been the areas most affected by subsidence (1–2 cm/year) since the 2000s; the overall subsidence rate and the size of subsiding area (>5 mm/year) have been decreasing as a result of the groundwater regulations enforced by HGSD and other local agencies. HoustonNet data and products are released to the public through HGSD. The primary products are the daily East-North-Up (ENU) position time series and site velocities with respect to the International GNSS Service (IGS) Reference Frame 2014 (IGS14), the stable Gulf of Mexico Reference Frame (GOM20), and the stable Houston Reference Frame (Houston20). The ENU position time series with respect to Houston20 are recommended for delineating subsidence and faulting within the Greater Houston region. The ENU time series with respect to GOM20 are recommended for studying subsidence and faulting within the Gulf coastal plain and sea-level changes along the Gulf Coast. The entire HoustonNet data set is reprocessed every a few years with updated positioning software, IGS and regional reference frames, and data analysis tools. We recommend that users use the most recent release of HoustonNet data products and avoid mixing old and new positions. [ABSTRACT FROM AUTHOR]

Abstract Technique PPP–RTK combines the advantages of both the Precise Point Positioning (PPP) and the Real-Time Kinematic (RTK) positioning. With the emergence of multi-frequency Global Navigation Satellite System (GNSS) observations, it is preferable to formulate PPP–RTK functional models based on original (undifferenced and uncombined) observations. While there exist many variants of the undifferenced and uncombined PPP–RTK models, a unified theoretical framework needs developing to link these variants. In this contribution, we formulate a class of undifferenced and uncombined PPP–RTK functional models in a systematic way and cast them in a unified framework. This framework classifies the models into a code-plus-phase category and a phase-only category. Each category covers a variety of measurement scenarios on the network side, ranging from small-, medium- to large-scale networks. For each scenario, special care has been taken of the distinct ionospheric constraints and the difference between Code Division Multiple Access (CDMA) and Frequency Division Multiple Access (FDMA) signals. The key to systematically formulating these models lies in how to deal with the rank deficiency problems encountered. We opt for the Singularity-basis (S-basis) theory, giving rise to the full-rank observation equations in which the estimable parameters turn out to be the functions of original parameters and those selected as the S-basis. In the sequel, it becomes straightforward to derive for each scenario the user model as it, more or less, amounts to the single-receiver network model. Benefiting from the presented theoretical framework, the relationships and differences between various undifferenced and uncombined PPP–RTK models become clear, which can lead to the better use of these models in a specific situation.

GNSS receivers with multi-system and multi-frequency capabilities allow more reliable positioning, especially in challenging environments. However, increased available systems and signals lead to hardware resource allocation problems. This issue escalates when ten correlators per tracked GNSS signal are used. Moreover, the communication interface between the PL and the PS affects the time complexity and challenges the capability of the maximum number of tracking channels. This paper presents the GOOSE v2, a MPSoC-based GNSS receiver, which is composed of a quad-band (L1, L2, L5/E5 and S band) RFFE and baseband digital signal processing. The SoC includes a quad-core 64-bit processor and an FPGA. The on-chip communication between the processors and the FPGA offers high bandwidth, significantly reducing the time complexity. The preliminary evaluation of this new receiver platform shows that, in addition to the legacy signals, it supports NavIC L5 and S-band signals. Furthermore, this receiver closes the tracking loops faster than its legacy version (GOOSE v1), opening the door to implementing more complex algorithms requiring higher time complexity.

Germany has a wide-meshed waterway network. Although inland navigation is one of the most efficient transportation methods, its share of transport is steadily decreasing. The research project Smart-Port-Shuttle Hildesheim aims to strengthen the waterway system both from the aspect of inland navigation and from the aspect of ports. For this purpose, a holistic approach is chosen, which includes technological innovations on the one hand and integration into a logistical concept on the other. This paper focuses on the first point: a technological approach for a (partially) automated inland vessel. It will present the hardware setup and an evaluation of the sensors at a selection of critical points, such as in a lock, at a bridge passage, or during the passing of an obstacle. Different methods, such as Galileo E5a measurements for positioning or Simultaneous Localization and Mapping (SLAM) for constructing and updating the map of the branch canal, are analyzed. The data structure of the Robot Operating System (ROS) is presented, as the raw data are available to the audience.

Spoofing interference is one of the most emerging threats to the Global Navigation Satellite System (GNSS); therefore, the research on anti-spoofing technology is of great significance to improving the security of GNSS. For single spoofing source interference, all the spoofing signals are broadcast from the same antenna. When the receiver is in motion, the pseudo-range of spoofing signals changes nonlinearly, while the difference between any two pseudo-ranges changes linearly. Authentic signals do not have this characteristic. On this basis, an anti-spoofing method is proposed by jointly monitoring the linearity of the pseudo-range difference (PRD) sequence and pseudo-range sum (PRS) sequence, which transforms the spoofing detection problem into the sequence linearity detection problem. In this paper, the model of PRD and PRS is derived, the hypothesis based on the linearity of PRD sequence and PRS sequence is given, and the detection performance of the method is evaluated. This method uses the sum of squares of errors (SSE) of linear fitting of the PRD sequence and PRS sequence to construct detection statistics, and has low computational complexity. Simulation results show that this method can effectively detect spoofing interference and distinguish spoofing signals from authentic signals.

In recent years, real-time global ionospheric map (RT-GIM) products have been actively developed by the international global navigation satellite system (GNSS) service (IGS) and its ionosphere associate analysis centers (IAACs) along with the increase of RT-GNSS multi-frequency and multi-constellation observations. In this study, the accuracy and consistency of three RT-GIM products from the Chinese Academy of Sciences (CAS), Wuhan University (WHU), and IGS are evaluated and analyzed utilizing three validation methods, namely, comparison with JASON-3 vertical total electron content (VTEC), the difference of slant total electron content (dSTEC), and IGS combined final GIM (IGSG) data. The test period was from 1 January 2019 to 31 December 2022, including the different solar activities. First, the comparison with JASON-3 data illustrates that the quality of the three RT-GIM products over oceans is in great consistency with that of the IGSG during different levels of solar activity and the daily mean bias (MEAN) values in low and high solar activities are approximately 5 and 10 TECU, respectively. The root mean square (RMS) values under low and high solar activities can be up to 7 and 12 TECU. Furthermore, the dSTEC validation results present that the MEAN values of RT-GIM products from different IAACs at high- and mid-latitude stations are about 0.5 TECU, which is smaller than those at low-latitude stations at about 1 TECU over continental regions. The standard deviation (STD) and RMS values for various RT-GIM products are within 3 and 4 TECU at low latitudes, respectively. In terms of the comparison with IGSG, the result shows that IGS combined RT-GIM (IRTG) presents better consistency than CAS RT-GIM (CRTG) and WHU RT-GIM (WRTG) in 2021 and 2022, with average annual STD and RMS values of 2.56 and 2.78 TECU, respectively. The daily biases of the RT-GIM products relative to IGSG can reach 4 TECU in high solar activities and the daily STD and RMS values are mainly within the 5 to 6 TECU range, respectively.

In recent years, the most popular methods for determining coastline course are geodetic, satellite, and tacheometric techniques. None of the above-mentioned measurement methods allows marking out the shoreline both in an accurate way and with high coverage of the terrain with surveys. For this reason, intensive works are currently underway to find alternative solutions that could accurately, extensively, and quickly determine coastline course. Based on a review of the literature regarding shoreline measurements, it can be concluded that the photogrammetric method, based on low-altitude images taken by an Unmanned Aerial Vehicle (UAV), has the greatest potential. The aim of this publication is to present and validate a method for determining coastline course based on low-altitude photos taken by a drone. Shoreline measurements were carried out using the DJI Matrice 300 RTK UAV in the coastal zone at the public beach in Gdynia (Poland) in 2023. In addition, the coastline course was marked out using high-resolution satellite imagery (0.3–0.5 m). In order to calculate the accuracy of determining the shoreline by photogrammetric and satellite methods, it was decided to relate them to the coastline marked out using a Global Navigation Satellite System (GNSS) Real Time Kinematic (RTK) receiver with an accuracy of 2.4 cm Distance Root Mean Square (DRMS). Studies have shown that accuracies of determining coastline course using a UAV are 0.47 m (p = 0.95) for the orthophotomosaic method and 0.70 m (p = 0.95) for the Digital Surface Model (DSM), and are much more accurate than the satellite method, which amounted to 6.37 m (p = 0.95) for the Pléiades Neo satellite and 9.24 m (p = 0.95) for the Hexagon Europe satellite. Based on the obtained test results, it can be stated that the photogrammetric method using a UAV meets the accuracy requirements laid down for the most stringent International Hydrographic Organization (IHO) order, i.e., Exclusive Order (Total Horizontal Uncertainty (THU) of 5 m with a confidence level of 95%), which they relate to coastline measurements.

With the rapid development of multi-mode and multi-frequency GNSSs (including GPS, GLONASS, BDS, Galileo, and QZSS), more observations for research on ionosphere can be provided. The Global Ionospheric Map (GIM) products are generated based on the observation of multi-mode and multi-frequency GNSSs, and comparisons with other GIMs provided by the ionosphere analysis centers are provided in this paper. Taking the CODE (Center of Orbit Determination in Europe) GIM as a reference during 30 days in January 2019, for the GIMs from JPL (Jet Puls Laboratory), UPC (Technical University of Catalonia), ESA (European Space Agency), WHU (Wuhan University), CAS (Chinese Academy of Sciences), and MMG (The multi-mode and multi-frequency GNSS observations used in this paper), the mean bias with respect to CODE products is 1.87, 1.30, −0.10, 0.01, −0.02, and −0.71 TECu, and the RMS is 2.12, 2.00, 1.33, 0.88, 0.88, and 1.30 TECu, respectively. The estimated multi-type DCB is also in good agreement with the DCB products provided by the MGEX.

A global navigation satellite systems (GNSS) real-time kinematic (RTK) using multiple base stations is referred as an antenna array-aided RTK. This positioning method more reliably resolves carrier phase integer ambiguities by processing additional measurements provided from multiple GNSS reference stations whose antenna locations are accurately surveyed. In this study, a conventional antenna array-aided RTK is extended to improve RTK performance with multiple moving base stations. Because accurately surveyed antenna coordinates are not available, we propose an antenna array-aided Kalman filter approach to first check the sanity of the fixed integer ambiguities and baselines estimated by moving reference stations and use them as a-priori information in RTK measurement formulations. Our flight test results with three unmanned aerial vehicles confirmed that the proposed Kalman filter formulation outperformed the conventional RTK and a priori antenna array-aided RTK approaches in the aspects of positioning accuracy, time to first fix, and success probability of integer ambiguity fixes.

CryoSat-2 repeatedly collects dense radar altimetry footprints covering high latitudes of the polar ice sheets for over ten years. The Baseline-D height product of CryoSat-2 was recently upgraded and released in 2019. Based on the internal or locally external evaluation of CryoSat-2 Baseline-D ice heights, we extended the validation both heterogeneously and spatially to accomplish a comprehensive assessment. First, reliable ice surface global navigation satellite system point solutions along the 36th Chinese National Antarctic Research Expedition traverse functioned as a height reference for comparison with the CryoSat-2 SAR synthetic aperture radar interferometric mode (SIN) data. Secondly, the Land Ice Along-Track Height product over Lambert–Amery system (LAS) from ice, cloud, and land elevation satellite 2 (ICESat-2), was also applied to validate CryoSat-2 SIN and low-resolution mode (LRM) data. As the results indicate, the SIN height accuracy is evaluated to be −1.68 m ± 2.35 m (slope < 0.65°) along the traverse and −0.96 ± 1.95 m (slope < 0.95° and roughness < 3 m) over the margin of LAS. The LRM height accuracy is evaluated to be −0.13 ± 0.46 m (slope < 0.1° & roughness < 1 m) over the interior of LAS. The standard deviation of the height differences degrades linearly against the limited growth of the terrain slope/roughness at the confidence level of 99%. It is worth noting that the spatially heterogeneous pattern of height differences is correlated with surface topography variations. This article should imply the potential to improve the estimation of mass balance and its uncertainty of polar ice sheets based on radar altimetry data.

The ionospheric storm time responses during August 2018 are investigated over South American region using multiple observables, for example, Global Navigation Satellite System (GNSS) derived vertical total electron content (VTEC) from International GNSS Service, magnetic field data, geomagnetic indices, global ionospheric maps, thermospheric mass density (TMD), and [O/N2] ratio measurement. Strong-ionospheric and upper-atmospheric disturbances affected the ionospheric variables with long duration during the storm recovery phase and following after. First, daytime VTEC (9:00–20:00 UT) presented variations of >15 TECU during days 25 to 30 of August 2018 in low and middle latitudes of South America, this after sudden storm commencement (SSC). Furthermore, nighttime (21:00–24:00 and 00:00–05:00 UT) VTEC presented low values (58 TECU) in the recovery phase. Second, the ionospheric values during the storm main phase and following after, at low- and mid-latitudes, caused the equatorial ionization anomaly to expand due to prompt penetration electric field. Furthermore, VTEC enhancements are likely to occur few hours after the SSC of 25 August 2018, while enhancements of TMD and [O/N2] ratio started to appear later on 26 and 27 of August 2018.

A fully integrated GPS receiver system can enable unique system capabilities by synthesizing both receiver front end and baseband on the same chip, leading to lower area overhead and higher integration. A survey on the GPS receiver system that focuses on front end design is presented in the paper. The first section discusses the various global navigation satellite system (GNSS), followed by the GPS working section. Afterwards, the paper discusses the previous works on the GPS receiver front end design. It provides a detailed survey and classification of various receiver architecture, including the LNA, mixer, filter, and ADC topologies. Besides, several image rejection techniques are presented for more than 50 GPS receivers. The various performance parameters of the GPS receiver front end in the literature are presented with the graphical view in the state-of-the-art discussion section. This literature survey provides the most extensive compilation to date of the various topologies, techniques and explains their implementation in the GPS receiver system. A new figure of merit that includes all the system parameters is proposed. The FOM can be an excellent reference to enhance the research work in the field of GPS receivers. In the end, the paper describes the possible research scope and challenges associated with the design of the GPS receiver front end.

Global navigation satellite systems (GNSSs) are widely used in various fields to provide high-precision timing. However, their low signal power and open signal structure render GNSS receivers vulnerable to time synchronization attacks (TSAs). In this study, we propose a novel single-antenna-based TSA mitigation algorithm. First, a multicorrelator estimator was adopted to calculate the parameters of the authentic and counterfeit signals. Then, the predicted pseudorange was derived for spoofing validation, and the counterfeit signals were subtracted. Finally, a robust extended Kalman filter was adopted to resist gross errors in the estimation results. A series of Monte Carlo simulations were conducted to evaluate the performance of the proposed method with different relative delays and power advantages of the spoofing signals. The Texas Spoofing Test Battery spoofing datasets were used in the experiments to verify the effectiveness of the proposed algorithm. The results indicated that the timing errors decreased from $2.0~\mu \text{s}$ (600 m) to 53.4 ns (16 m), and the spoofing signals were considerably mitigated. The proposed anti-spoofing algorithm can ensure the availability of GNSS timing under TSAs and only requires a single antenna, greatly simplifying the GNSS receiver installation and reducing costs.

Power inversion algorithms (PI) are widely used by ant-jamming receivers in harsh environment, but it also induces pseudo-range measurement error which is fatal to high precision receivers. In this paper, it proposes a more accurate method based on the S Curve Bias (SCB) to evaluate the pseudo-range bias which can actually affect the positioning results, as it excludes a fixed time delay contained in the directly differing method. When using this method to assess the bias, it is found that pseudo-range errors can be influenced by many factors including the type of interference. Simulation as well as experiment based on a 4-element array antenna are designed and carried out. The simulation has shown that both SOP-PI and STAP-PI will bring bias to pseudo-range measurement. Specifically, the maximum bias caused by SOP-PI is 1m while the bias caused by STAP-PI is up to 10m. Moreover, comparing to the method which directly differs the pseudo-range of output signal and the reference signal, the method proposed in this paper has better performance. Furthermore, the experimental results verify the effectiveness of the proposed evaluating method.

Compared with the final global ionospheric maps (GIMs) product on a weekly basis, rapid GIMs products on a daily basis have significant improvement in the availability, and are an important ionospheric information source for timely studies of global, regional, or local space weather events and corresponding other applications. Therefore, it is essential to analyze and validate the performance of different rapid products. In this contribution, we evaluate the accuracy and consistency of nine rapid GIMs released by different international GNSS service (IGS) ionosphere associate analysis centers (IAACs) from various aspects in detail, including IGS final GIMs, JASON-2/-3 ionospheric data, the difference of slant total electron content (dSTEC) data over one solar cycle from January 2009 to December 2020. In comparison with the IGS final GIMs, the variation trend of the standard deviation (STD) is consistent with the solar activities, and accompanied by seasonal and annual periodic variations. The mean bias (MEAN) between IGS and individual IAAC is approximately −2 to 2 TECU, and the STD is approximately 0.7 to 4.0 TECU. Over oceanic regions, the MEAN values of the different rapid GIMs with regard to JASON-2 are about 2 to 3 TECU and that of JASON-3 are about 4 to 5 TECU, except for JPRG. The STD performance of the various GIMs relative to the JASON-2 and JASON-3 vertical total electron content (VTEC) is both within 3 TECU. Over continental regions, the validation with the dSTEC data shows that the STD values of the external and internal verification range from 0 to 4.5 TECU and from 0 to 3 TECU, respectively, and the STD performance of the UQRG is the best. [ABSTRACT FROM AUTHOR]

This article presents a global navigation satellite system (GNSS) based attitude (pitch, roll, and heading) determination method for spinning objects by estimating the unit vector along the projectile nose and the antenna pointing vector that lies radially in the plane of rotation. A theoretical framework of a spinning platform is developed by exploiting the periodic variations of the received signal. The presented method provides a drift-free attitude determination of spinning projectiles directly in earth-centered earth-fixed reference. The scheme may find a good combination with low-cost small projectiles where the provision of advanced inertial sensors is constrained by cost and installation space. The proposed solution can be adopted for different degrees of accuracy and computational complexity. The performance guarantee for the proposed algorithm is established using simulated data and real GNSS data. For real satellite signals, the performance results are validated using an experimental setup having a roll rate of around 750 r/min. [ABSTRACT FROM AUTHOR]

The inhomogeneous distribution of Global Navigation Satellite System (GNSS) stations results in inaccurate vertical total electron contents (VTECs) in global ionosphere maps (GIMs) over areas with large GNSS data gaps. Incorporating VTECs from the International Reference Ionosphere (IRI) model is usually adopted as one approach to mitigate the inaccurate VTECs. However, large and complicated spatiotemporal varying VTEC biases between GNSS and IRI suggest a robust strategy to optimally combine GNSS and IRI VTECs for operational high-precision modeling. Here, we thoroughly analyze the characteristics of VTEC biases between GNSS and IRI-2016 model in different latitudes from 2009 to 2019, and develop an improved functional and stochastic model. An automated assimilation strategy of GNSS and IRI-2016 VTECs is proposed for Shanghai Astronomical Observatory final GIM (SHAG) routine estimation, and the reliability of GIMs in areas with lack of stations is enhanced by attaching Virtual Observation Stations (VOSs) based on IRI-2016 model and VOS bias parameters. Experimental results show that the root-mean-square errors (RMSEs) of SHAG with respect to VTECs retrieved from four independent GNSS assessment stations are reduced by 21.65–53.06% in the large data gaps with the assistance of VOSs. Furthermore, we validated the long-term reliability of SHAG spanned one solar cycle (2009–2019) with International GNSS Service (IGS) final GIMs and satellite altimetry VTECs. Validation results suggest that SHAG is in good agreement with IGS final GIMs, and reliability of SHAG in large GNSS data gap areas is significantly improved by attaching VOSs and biases. This methodology also represents an efficient tool for automated global ionospheric modeling integrating multi-source data. [ABSTRACT FROM AUTHOR]

The global navigation satellite system (GNSS) measurements to determine ionospheric total electron content (TEC) are mainly derived from expensive geodetic-grade receivers, which are not conducive to high-density placement. In this work, we present an analysis of the performance of ionospheric TEC determined by GNSS dual-frequency measurements derived from the smartphone, taking the Xiaomi 8 (XMI8) as an example. First, the ionospheric observable is retrieved from the code and carrier phase data using the carrier-to-code leveling technique and a new carrier-to-noise weighting strategy instead of an elevation weighting strategy, considering the characteristic of the GNSS measurements from smartphones. Then, the absolute ionospheric slant TEC (STEC) values are isolated from the ionospheric observables by modeling with the generalized trigonometric series function. The experimental data, covering over 120 h, were taken from two situations: one is the data collected by the original smartphone antenna; the other is the external geodetic-grade antenna. The TEC data obtained from the collocated geodetic-grade receiver are used as reference data to evaluate the performance of the TEC values from XMI8. Compared to the reference data, the evaluation results show that the ionospheric STEC extraction accuracy can reach total electron content unit (TECU) values of 0.17 and 0.11 under the two different situations in the continuous carrier phase satellite arc without cycle slips. In addition, the VTEC modeling accuracy is above 5 and 2 TECU in the two different situations, respectively. Thus, we concluded that consumer-level GNSS chipsets are highly potential in the future to increase the ionospheric monitoring station density due to their low costs and good data quality. [ABSTRACT FROM AUTHOR]

The application of the global navigation satellite system (GNSS) in aviation effectively meets the requirements of aircraft location-based services. However, the GNSS application in airborne surveys mostly follows the land survey method, which ignores the particularity of the airborne flight operations, such as the rapid change of altitude difference. The present study contributes to this research trend by constructing a suitable positioning model for airborne kinematic precise positioning and analyzing the variation characteristics of receiver phase biases (RPBs) and tropospheric zenith wet delays (ZWDs) to explore their impact on airborne positioning. The ionosphere-weighted post-processing kinematic (PPK) model for long-distance high-altitude positioning is constructed by adding the between-station single-differenced ionospheric pseudo-observations. Aside from these, the correlation between the characteristics of RPBs, ZWDs, and environmental factors (temperature, vapor pressure) is analyzed and the kinematic constraint of the ionosphere-weighted PPK model on RPBs and ZWDs is adjusted based on their true variations. The results show that under the condition of large altitude differences, temperature-sensitive RPBs and ZWDs significantly impact positioning. Furthermore, pre-calibrating RPBs and ZWDs can significantly improve the ambiguity success rate and positioning performance. Based on the bias-calibrated ionosphere-weighted PPK model, the fixed solutions difference of the two common-antenna moving stations carried by the manned aircraft is stable for the positioning results of the two flight experiments. The 3-dimensional root-mean-square (3D-RMS) values are 1.4 cm and 0.5 cm, with 82% and 99% ambiguity success ratios. [ABSTRACT FROM AUTHOR]

On the 15th of March 2021, the strongest sandstorm in a decade occurred in northern China, and had a great adverse impact on the natural environment and human health in northern China. Real-time monitoring of dust storms is becoming increasingly important. In order to effectively analyze the non-hydrostatic delay (ZNHD) anomaly during a sandstorm, the method based on GNSS-derived tropospheric ZNHD residual to monitor the sandstorm is proposed at the same time. We studied the relationship between ZNHD/PWV and PM10/PM2.5 in Beijing, Changchun, Pingliang and Zhongwei before and after sandstorms. The ZNHD time series was then decomposed by singular spectrum analysis (SSA) and the residuals were obtained. The relationship between the GNSS-derived ZNHD residual and PM10 was analyzed. The results show that the impact of the sandstorm on PM10 is greater than that on PM2.5. Before the sandstorm, the correlation between PM10 and ZNHD was low, less than 0.25. When the sandstorm occurred, the correlation between PM10 and ZNHD increased significantly, and the maximum was greater than 0.7. When the sandstorm ended, the correlation between PM10 and ZNHD decreased significantly. Through the relationship between the ZNHD residual and PM10, it can be found that when the peak-to-peak values of the ZNHD residual are all above 80 mm, sandstorms may occur. But Rainfall, snowfall, haze and other abnormal weather can also lead to ZNHD anomalies. [ABSTRACT FROM AUTHOR]

Potential evapotranspiration (PET) is generally estimated using empirical models; thus, how to improve PET estimation accuracy has received widespread attention in recent years. Among all the models, although the temperature-driven Thornthwaite (TH) model is easy to operate, its estimation accuracy is rather limited. Although previous researchers proved that the accuracy of TH-PET can be greatly improved by using a limited number of variables to conduct calibration exercises, only preliminary experiments were conducted. In this study, to refine this innovation practice, we comprehensively investigated the factors that affect the calibration performances, including the selection of variables, seasonal effects, and spatial distribution of Global Navigation Satellite System (GNSS)/weather stations. By analyzing the factors and their effects, the following conclusions have been drawn: (1) an optimal variable selection scheme containing zenith total delay, temperature, pressure, and mean Julian Date was proposed; (2) the most salient improvements are in the winter and summer seasons, with improvement rates over 80%; (3) with the changes in horizontal (2.771–44.723 km) and height (1.239–344.665 m) differences among ten pairs of GNSS/weather stations, there are no obvious differences in the performances. These findings can offer an in-depth understanding of this practice and provide technical references to future applications. [ABSTRACT FROM AUTHOR]

We have proved in theory and simulation that the circularly polarized (CP) radiation from a resonant quadrifilar helix antenna (QHA) is independent of the phase between two composed bifilar helices, while the CP radiation pattern can be reconfigured by controlling this phase. It is demonstrated that the QHA is capable of generating a wide range of CP radiation patterns, including two cardioid patterns toward both ends, two vertical toroid patterns in two different orientations, and the mixed patterns of cardioid and toroid. A wideband and multilayer feeding network was developed to excite the four basic modes of the proposed QHA through phase control in the experiment. The simulated and measured right-handed circular polarization (RHCP) radiation patterns agree quite well. The proposed QHA was also designed to cover several global navigation satellite system (GNSS) bands. The measured impedance bandwidth is 1.26–1.65 GHz (26.8%) and the 3 dB axial ratio bandwidth is 1.1–1.8 GHz (41.4%) in the endfire mode. It is shown that the proposed QHA can be easily operated in two endfire modes to have an equivalent isotropic pattern for flying objects. When the antenna is operating in the endfire mode, the gain is in the range of 2.1–3.5 dBi, and the radiation efficiency is 70%–85% in the operating band. [ABSTRACT FROM AUTHOR]

Objectives: It is difficult to effectively evaluate and weaken the impact of observation environment change on data quality. To solve the problem, this paper studies a multi-point hemispherical grid model which can be applied to various satellite navigation systems.Methods: It integrates the observations of different global navigation satellite system (GNSS) satellite pairs in different observation periods to establish a global optimal multipath model. GNSS multipath error can be estimated effectively using multi-point hemispherical grid model (MHGM), image-based inverse of multipath effect and reflect the orientation of interference sources in the observation environment around stations. Results: To a certain extent the observation environment around stations can be modelled. The test under deliberate high multipath environment reveals that using MHGM significantly improved the root mean square error (RMSE) of the double-differenced carrier phase observation residuals in the period of fixed ambiguity, reducing the mean RMSE from 0.983 cm to 0.318 cm, a 67.7% improvement. In addition, the abnormal areas with large value of correction in the MHGM results is consistent with the direction of the baffle mounted at the corresponding stations. This model indicates multipath effects on GNSS carrier phase observations in different directions from the station. The test with International GNSS Service historical observations shows MHGM can effectively reflect the influence of changing multipath interference around stations on carrier phase observations, with an average improvement of 29.5% in the RMSE of residuals over the past 18 years. Conclusions: This model can be used to evaluate the changes in observation environments surrounding stations over time. It has potential application value to improve the accuracy and reliability of augmentation system products. [ABSTRACT FROM AUTHOR]

Although a dynamic or semi-dynamic datum has been adopted in some countries, it remains a challenge if a long-term stable datum is to be established in a tectonic active area. This study presents an approach to realistically reflect the time dependent behaviors of ground reference points while maintaining the long-term stability of a datum. An adaptive approach coupled with the Euler motion model is proposed for dividing an area into blocks. A least-squares collocation is then applied for modeling the residual velocities in each block. A case study using the data from 375 continuously operated GNSS stations in Taiwan is presented. It is illustrated that the complex surface kinematics in this region can be divided into three blocks. Significant reductions up to 64% of residual velocities were obtained. This shows that a stable datum can be established in a region with active and complicated surface kinematics by implementing the proposed. [ABSTRACT FROM AUTHOR]

A flash flood is a rapid and intense response of a drainage area to heavy rainfall events. In the arid and semiarid parts of the Eastern Mediterranean (EM) region, the spatiotemporal distribution of rainfall is the most important factor for flash flood generation. A possible precursor to heavy rainfall events is the rise in tropospheric water vapor amount, which can be remotely sensed using ground-based global navigation satellite system (GNSS) stations. Here, we use the precipitable water vapor (PWV) derived from nine GNSS ground-based stations in the arid part of the EM region in order to predict flash floods. Our approach includes using three types of machine learning (ML) models in a binary classification task, which predicts whether a flash flood will occur given 24 h of PWV data. We train our models with 107 unique flash flood events and vigorously test them using a nested cross-validation technique. The results indicate a good agreement between all three types of models and across various score metrics. In addition, the models are further improved by adding more features such as surface pressure measurements. Finally, a feature importance analysis shows that the most important features are the PWV values from 2 to 6 h prior to a flash flood. These promising results indicate that it is possible to augment the current flash flood warning systems with a near real-time GNSS ground-based data-driven approach as demonstrated in this work. [ABSTRACT FROM AUTHOR]

Throughout the past few decades, advancements in global navigation satellite systems (GNSS), such as the Global Positioning System (GPS), have resulted in real-time planimetric accuracy at the centimetre level, whether achieved using Real-Time Kinematic (RTK) or Real-Time Network (RTN) approaches. This study examines and characterizes the performance of RTK and RTN solutions at three test sites in Los Angeles County, California, in the United States of America. The solutions were characterized to examine the dispersion of vertical measurements in the context of different environments. The results from this study suggest that both methods have advantages and drawbacks; in particular, the data showed that the distribution and density of the network stations, cellular network coverage and environmental dynamics have significant impacts on RTN vertical precision, where the vertical precision for the RTN measurements were observed to be 2–4 times lower in comparison to the RTK measurements. [ABSTRACT FROM AUTHOR]

In order to improve the efficiency of electrified vehicle drives, various predictive energy management strategies (driving strategies) have been developed. This article presents the extension of a generic prediction approach already proposed in a previous paper, which allows a robust forecasting of all traction torque-relevant variables for such strategies. The extension primarily includes the proper utilization of map data in the case of an a priori known route. Approaches from Artificial Intelligence (AI) have proven to be effective for such proposals. With regard to this, Recurrent Neural Networks (RNN) are to be preferred over Feed-Forward Neural Networks (FNN). First, preprocessing is described in detail including a wide overview of both calculating the relevant quantities from global navigation satellite system (GNSS) data in several steps and matching these with data from the chosen map provider. Next, an RNN including Long Short-Term Memory (LSTM) cells in an Encoder–Decoder configuration and a regular FNN are trained and applied. The models are used to forecast real driving profiles over different time horizons, both including and excluding map data in the model. Afterwards, a comparison is presented, including a quantitative and a qualitative analysis. The accuracy of the predictions is therefore assessed using Root Mean Square Error (RMSE) computations and analyses in the time domain. The results show a significant improvement in velocity prediction with LSTMs including map data. [ABSTRACT FROM AUTHOR]

NeQuick2 is a three-dimensional ionospheric electron density empirical model that uses numerical integration to calculate the total electron content along any line-of-sight (LOS). As one of the most commonly used three-dimensional ionospheric models, it is necessary to objectively evaluate the accuracy and stability of NeQuick2 over a long period, especially over the mid-latitudes of the northern hemisphere where most of the ground-based GNSS stations are distributed. Therefore, different methods are used in this study to evaluate the accuracy of the NeQuick2 model from 2008 to 2021, including comparison with the International Global Navigation Satellite System Global Ionosphere Maps (IGSG), Jason2 Vertical Electron content (VTEC), and self-consistent evaluation. The comparison with IGSG shows that the standard deviation (STD) value is about 2.59 TECU. The accuracy of the IGSG and NeQuick2 model over ocean regions shows that the bias of IGSG is more significant than that of the NeQuick2 model. The mean STD value is 2.09 TECU for IGSG, and the corresponding value is 3.18 TECU for the NeQuick2 model, which is about 50% worse than IGSG. The dSTEC assessment results indicate that the variation in bias for IGSG is more stable than that of the NeQuick2 model. The mean STD value is 0.86 and 1.52 TECU for IGSG and NeQuick2 model, respectively. The conclusion could be made that NeQuick2 model represents the average ionosphere electron content and its accuracy fluctuates with solar conditions. Compared with the IGSG, the NeQuick2 model always underestimates TEC value, especially in low solar activity periods and compared with Jason2, the TEC values obtained by NeQuick2 model are overestimated, but the degree of overestimation is smaller than that of IGSG. [ABSTRACT FROM AUTHOR]