Tightly Coupled GNSS with Stereo Camera Navigation Using Graph Optimization

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.

The study of climate change is an important field of research. Monitoring of atmospheric variability especially the tropospheric precipitable water vapor (PWV) is a powerful way to investigate climate change. Global navigation satellite systems (GNSS) provide a good tool for studying atmospheric parameters as GNSS signals along its path from the satellites to the ground based receivers suffer a significant delay due to the refractivity of earth’s atmosphere. GNSS signals are not only delayed but also refracted in the neutral atmosphere. The zenith wet delays (ZWD) caused by the troposphere can be estimated during the geodetic processing of GNSS signals. Since the ZWD is tightly correlated to the PWV, GNSS observations can be used to study the changes of PWV. In this study GNSS data from the Egyptian permanent GNSS network (EPGN), International GNSS Service (IGS), EUREF Permanent Network (EPN) and Scripps Orbit and Permanent Array Center (SOPAC), for the years 2013 and 2014, were used. These GNSS stations provided a good geometrical coverage over the respective region. All GNSS data were processed using Bernese V 5.2 software. The needed product from GNSS data processing is the tropospheric zenith total delay (ZTD) over each GNSS site. The ZTD is divided based on physical parameters into zenith hydrostatic delay (ZHD) and zenith wet delay (ZWD). The ZWD is the basic observable used to calculate PWV. The values of the PWV were calculated and its variation over the study area was investigated. The quality of calculated PWV values from GNSS data evaluated against traditional Radiosonde (RS) measurements. The results of GNSS PWV showed good agreement with RS PWV when the PWV values were low, but this agreement became worse at high PWV values as the differences between the two techniques increased. The correlation coefficient between RS PWV and GNSS PWV varied from 0.31 to 0.84. Standard deviation of the differences between RS PWV and GNSS PWV ranged from 2.369 to 5.973 mm. The PWV estimated from GNSS observations had annual cycle, and its cycle in 2013 was different from that in 2014. The PWV differences between the 2 years clarified that the water vapor content over the east Mediterranean in most of 2014 days was higher than that in 2013. Furthermore, PWV variations were noted on both temporal and spatial scales. The highest temporal variation value was 25.41 mm whereas the maximum value of the spatial variation was 19.67 mm. The present study illustrated the importance of using geodetic networks to provide atmospheric information.

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.

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

Global Navigation Satellite System (GNSS) data can be used in a myriad of ways. The current number of applications exceed by far those originally GNSS was designed for. As an example, the present Special Issue on GNSS Data Processing and Navigation compiles 14 international contributions covering several aspects of GNSS research. This Editorial summarizes the whole special issue grouping the contributions under four different, but related topics.

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 impact of L‐band equatorial ionospheric scintillation on Global Navigation Satellite Systems (GNSS) receivers is investigated in this paper using both real and synthetic scintillation data. To this end, various low‐latitude data sets, recorded during the most recent solar maximum, are exploited in post‐processing to develop and verify realistic simulation tools and evaluate GNSS receiver performance. A scintillation simulation model is implemented based on the phase screen formulation of Dr. Charles Rino (1979, 1982, and 2011) which allows oblique signal propagation in an anisotropic propagation medium with multiple irregularity layers (or phase screens) for multiple GNSS frequencies. The observed real scintillation parameters are used to drive GNSS signal simulations. The subsequent simulated GNSS signal time series are verified through comparison with real data for different signal tracking states including the most severe and challenging tracking scenarios. Using both real and synthetic data sets, the impact of scintillation on observation quality and receiver performance is evaluated in terms of probability of loss of phase and frequency lock, as well as the correlation of disturbed L‐band signals transmitted by GNSS satellites on the same transionsopheric path.

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.

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.

This study evaluates the suitability of compact, lowcost Global Navigation Satellite System (GNSS) receiver module in estimating the effects of tropospheric delay on accuracy of GNSS position solution at GNSS laboratory, the University of Burdwan, West Bengal, India with geographical coordinate of 23.2545°N, 87.8467°E, −11.402 m, using compact, low-cost uBlox F9P GNSS module and high cost Leica GR 50 geodetic receiver in March 2023. GNSS observation data were logged by the two receivers for 24 hours at 1Hz dual-frequency and converted into Rinex observation files in GPS-only, GLONASS-only, and GPS+GLONASS hybrid modes. The converted Rinex observation data were uploaded to the Natural Resources of Canada (NRCan) online service to obtain the user fixed values of latitude, longitude and attitude, and the wet and dry tropospheric delay files via email. The converted Rinex observation files were post-processed using GNSS positioning analysis tool that estimate the GNSS positioning accuracy parameters namely Distance Root Mean Square (2DRMS), Circle of Error Probable (CEP), Spherical Error Probable (SEP), and Mean Radian Spherical Error (MRSE) from the users defined known fixed values of latitude, longitude and altitude. The total delay obtained from the two GNSS receivers were compared with their positioning accuracy parameters. The results showed better position solution on observation days with the least total zenith delay in all the constellations considered. The Leica GR 50 shows an average total zenith delay of 2.476 m in GPS mode, 2.486 m in GLONASS mode and 2.4680 m in GPS+GLONASS hybrid mode while the uBlox F9P shows 2.477 m in GPS mode, 2.494 m in GLONASS mode and 2.4684 m in GPS+GLONASS hybrid mode of operation. The GPS+GLONASS hybrid mode of operation reduces the total zenith delay and enhanced the position solution. Hence, there is no distinct difference between the two GNSS receivers, suggesting that compact, low-cost, GNSS receiver modules can be used for tropospheric study.

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.

Tropospheric delay is an important error source in Global Navigation Satellite System (GNSS) positioning and can also be used in water vapor monitoring. Many models have been built to correct tropospheric delays or to convert zenith wet delays to precipitable water vapor. However, these models suffer from limited resolutions (spatial resolution lower than 1° and temporal resolution lower than 6 hr), which affects their performance. The release of European Centre for Medium‐Range Weather Forecasts ReAnalysis 5 (ERA5) provides the opportunity to lift this limit. In this study, we use the ERA5 hourly 0.5° × 0.5° data to build a new model over China, which integrates tropospheric delay correction for GNSS positioning and weighted mean temperature calculation for GNSS meteorology. By modeling the diurnal variations of zenith hydrostatic delay, zenith wet delay, and weighted mean temperature and the seasonal variations in their lapse rates, this model has the state‐of‐the‐art spatial resolution of 0.5° × 0.5° and temporal resolution of 1 hr. We validate this new model by the ERA5 data, the radiosonde data, and the GNSS data in comparison with the canonical GPT2w model. The results show that the new model has better accuracies in terms of root‐mean‐square than the GPT2w model in all parameters. Especially, the new model well captures the diurnal variations in tropospheric delay and weighted mean temperature. This new model provides accurate tropospheric delays and weighted mean temperature simultaneously, which enables GNSS receivers to measure precipitable water vapor directly and also benefits GNSS positioning.

<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>

Text: Positioning, timing and navigation (PNT) with Satellite Navigation Systems like GPS, GLONASS, Beidou and Galileo are more and more widespread and used in very different types of applications including core telemetry applications. Global Navigation Satellite Systems (GNSS) receivers in Safety Critical Applications such as landing airplanes, Critical Infrastructure or Remote Sensing missions e.g. Earth Observing Satellites need to be very robust and reliable under a variety of environmental conditions, sometimes very harsh ones outside the atmosphere. Timing is critical in communication between satellite ground stations and the satellites, e.g. telemetry tracking and command, or communication protocols using Time Division Multiple Access. Thus, it is crucial to test GNSS receivers thoroughly under a variety of different conditions, also extreme conditions like very high dynamics or unhealthy GNSS satellites. For this purpose GNSS RF signal simulators are a versatile and flexible tool. The tests can be repeated as many times as necessary with identical conditions. Besides this a simulator offers complete control across the scenario, every single detail can be controlled and changed. In RF simulators comprehensive error models are available for satellite signals and clocks, satellite orbits and health flags, obscuration and multipath, atmospheric conditions, antenna characteristics, vehicle dynamics, leap seconds, jamming and aiding Inertial sensors. Each of them can be controlled individually. For some tests, like unhealthy satellites or future constellations RF, simulators are the only way for testing, as future signals, for example, are not available in live sky test. In this paper, we will introduce the capabilities of GNSS simulators with a wide range of different error conditions and show several telemetry use cases.

RTKLIB which is an open source Global Navigation Satellite Systems (GNSS) software has gained rapid acceptance among Surveying professionals thanks to recent developments in UAS (Unmanned Aerial System) technology. RTKLIB enables standard and precise point positioning (PPP) in real-time and post-processing modes to be performed. As such, UAS users utilize this software to analyze GNSS data collected by GNSS systems on UAS. By being versatile and free, RTKLIB is commonly used by many; however, it is not the only freely available GNSS software. There are also freely available online GNSS data processing software running on servers. These online GNSS data processing services provide data processing in static, kinematic and rapid static modes. Because UAS collect data in kinematic mode, in this study, kinematic data processing by aforementioned software (CSRS-PPP, GAPS and APPS) is analyzed. The results coming from these software are compared against the results produced by photogrammetric software (Agisoft Metashape and Pix4Dmapper). The aim of this practical project is to produce generalizable knowledge about the performance of these software. It is found out that RTKLIB and CSRS-PPP achieved cm-level precision. Yet, GAPS and APPS achieved dm-level precision both for horizontal and vertical coordinates. This study demonstrates the precision and accuracy expected from these software if they are used for kinematic GNSS data processing.