Raw Global Navigation Satellite System Measurements Research Articles

FGO-GIL: Factor Graph Optimization-Based GNSS RTK/INS/LiDAR Tightly Coupled Integration for Precise and Continuous Navigation

As a relative positioning technique, LiDAR-Inertial Odometry (LIO) is known to suffer from drifting and can only provide local coordinates. To compensate for these shortages of LIO, an effective way is to integrate global navigation satellite system (GNSS) with LIO. In this contribution, we proposed a tightly coupled GNSS RTK/INS/LiDAR system under the factor graph optimization framework, termed as FGO-GIL, to achieve high-precision and continuous navigation in urban environments. This integration system fuses raw GNSS measurements (i.e., pseudorange and carrier phase measurements) with IMU and LiDAR information at the observation level atop a factor graph. Moreover, a keyframe-based nonlinear optimization scheme is designed to efficiently utilize measurements from the mixed heterogeneous sensors. Specifically, non-keyframes are united with IMU pre-integration for inter-frame optimization, which can provide accurate and high-frequency state predictions for the scan-matching of keyframes. Sparse keyframes are used to construct LiDAR factors through matching with the submap for sliding window optimization. To evaluate the effectiveness of our approach, real-world experiments are conducted in both campus and urban environments. The results demonstrate that our system can achieve continuous decimeter-level positioning accuracy in these complex environments, outperforming other state-of-the-art frameworks.

A Data Quality Assessment Approach for High-Precision GNSS Continuously Operating Reference Stations (CORS) with Case Studies in Hong Kong and Canada/USA

Centimeter-level or better positioning accuracy is needed in engineering surveying applications. When employing the Global Navigation Satellite System (GNSS) in engineering surveying, the high-precision real-time kinematic (RTK) positioning method must be used to achieve such positioning accuracy. Currently, precise point positioning (PPP) cannot reliably achieve the positioning accuracy needed for engineering surveying in real time. The high-precision RTK positioning method needs carrier-phase measurements and a reference station/network. Surveyors may not need a GNSS receiver in their organizations/companies to act as the reference station. Continuously Operating Reference Stations (CORS), run by international/national organizations/agencies or private companies such as GNSS receiver manufacturers, let users freely access the raw GNSS measurements or corrections for real-time and post-processing applications. The positioning accuracy of the GNSS rover is affected by the data quality of the reference stations, including virtual reference stations (VRS). The International GNSS Service (IGS) currently provides the number of cycle slips and the L1 and L2 average pseudorange multipath errors per station daily. The US National Geodetic Survey (NGS) provides daily station coordinate residuals. Carrier-phase data quality of the CORS stations is not provided by their organizations/agencies. Nowadays, many CORS stations track multi-GNSS satellites. This paper proposes a multi-GNSS and multi-frequency data quality assessment approach for CORS stations with a focus on carrier-phase data quality. The proposed approach is demonstrated with case studies on IGS/CORS networks in Hong Kong and Canada/USA. In other words, a strategy to obtain non-linear combined carrier-phase multipath errors and noise is proposed in this work. The data quality of a CORS station depends on the site environment, monument type and height, and GNSS receiver/antenna. An account of the data quality at some selected stations is given; the main focus of the paper is on the proposed data quality assessment approach.

A Novel GNSS Fault Detection and Exclusion Method for Cooperative Positioning System

Global Navigation Satellite System (GNSS)-based cooperative positioning is highly degraded in dense urban areas due to non-line-of-sight (NLOS) and multipath effects. Multiple simultaneous faulty GNSS measurements can result in traditional fault detection and exclusion (FDE) methods finding a consistent but erroneous group of satellites. This paper describes a novel distributed FDE method based on cooperative reliability message (CRM), which is suitable for cooperative positioning system in connected vehicles. The idea is to take into account not only the consistency check on raw GNSS measurements, but also the prior knowledge about the quality of GNSS measurements offered by nearby cooperators so as to improve the ability to exclude multiple GNSS faults. The line-of-sight (LOS) satellites identified by cooperators are stored in CRMs, which will be shared to the host vehicle to generate the candidates for the set of LOS satellites observed locally. Based on the candidates, a bottom-up searching strategy is designed to find the healthy satellites and isolate the faulty ones precisely. Several experiments involving four moving vehicles were conducted in dense urban areas. Results indicate the superiority of CRM-based method in excluding multiple faults over existing methods.

Stochastic Modeling of Smartphones GNSS Observations Using LS-VCE and Application to Samsung S20

In recent years, numerous smartphones have been equipped with global navigation satellite system (GNSS) technology, enabling individuals to utilize their own devices for positioning and navigation purposes. In 2016, with the launch of a mobile app by Google, namely GnssLogger, smartphone users with Android version 7 or higher were able to record raw GNSS measurements (i.e., pseudorange, carrier phase, Doppler, and carrier-to-noise density ratio (C/N0)). Since then, enhancing the accuracy and efficiency of smartphone positioning has become an interesting area of research. Precise point positioning (PPP) is a powerful method providing precise real-time positioning of a single receiver, and it can be applied to smartphone observations as well. Achieving high-precision PPP requires selecting appropriate functional and stochastic models. In this study, we investigate the development of more reliable stochastic models for smartphone GNSS observations. The least-square variance component estimation (LS-VCE) method is applied to double-difference (DD) pseudorange and carrier phase observations from two Samsung S20s to obtain appropriate variances for GPS and GLONASS. According to the results, there is no significant correlation between the pseudorange and carrier phase observations of GPS and GLONASS on the L1 frequency. Furthermore, the quality of GLONASS carrier phase observations is comparable to that of GPS. The model's performance is then assessed with respect to single-frequency precise point positioning (SF-PPP) using a dataset collected in kinematic mode from a Samsung S20 smartphone. A significant improvement of 25.1% and 32.7% on the root-mean-square (RMS) of horizontal positioning and the 50th percentile error, respectively, was achieved when employing the obtained stochastic model.

Low-Cost COTS GNSS Interference Monitoring, Detection, and Classification System

Interference signals cause position errors and outages to global navigation satellite system (GNSS) receivers. However, to solve these problems, the interference source must be detected, classified, its purpose determined, and localized to eliminate it. Several interference monitoring solutions exist, but these are expensive, resulting in fewer nodes that may miss spatially sparse interference signals. This article introduces a low-cost commercial-off-the-shelf (COTS) GNSS interference monitoring, detection, and classification receiver. It employs machine learning (ML) on tailored signal pre-processing of the raw signal samples and GNSS measurements to facilitate a generalized, high-performance architecture that does not require human-in-the-loop (HIL) calibration. Therefore, the low-cost receivers with high performance can justify significantly more receivers being deployed, resulting in a significantly higher probability of intercept (POI). The architecture of the monitoring system is described in detail in this article, including an analysis of the energy consumption and optimization. Controlled interference scenarios demonstrate detection and classification capabilities exceeding conventional approaches. The ML results show that accurate and reliable detection and classification are possible with COTS hardware.

An improved phase-smoothed-code algorithm using GNSS dual-frequency carrier epoch-difference geometry-free combination observations

The Hatch filter has been widely used in global navigation satellite system (GNSS) single point positioning (SPP). However, the classic phase-smoothed-code (PSC) method is vulnerable to ionospheric cumulative errors, resulting in filter divergence and accuracy degradation in the positioning solution. In this paper, an improved method, i.e, phase epoch-difference geometry-free (GF) combination smoothed code (PESC), is proposed for SPP with raw GNSS measurements. In other words, the dual-frequency carrier-phase observations are used to form epoch-difference GF combinations to obtain the precise between-epoch variations of ionospheric delays. Then, the ionospheric delays of code observations during a continuous satellite arc are converted to the initial epoch’s value. Subsequently, the initial epoch’s ionospheric delay of each arc, setting as a constant value, is estimated along with other parameters through the Kalman filter. GNSS measurements from 20 globally distributed stations from multi-GNSS experiment (MGEX) network on July 7 2021 are selected to validate the feasibility of the new algorithm. Results indicate that the average root mean square (RMS) of PESC-SPP errors is 0.404 m, 0.340 m, and 1.491 m in east (E), north (N), and Up (U) components, respectively, with the improvement of 30.5%, 50.5%, 52.1% and 36.5%, 55.1%, 54.3% in comparison with that of PSC-SPP and SPP, respectively. Data collected in the kinematic experiment on 28th November 2021 is used to evaluate the PESC positioning performance. Results demonstrate that the deviations of epoch-difference slant total electron content (STEC) derived from the PESC-SPP are much smoother and more stable than those of PSC-SPP during continuous satellite arcs, with the average RMS improvements of 33.5%, 66.9%, and 32.9% for GPS, Galileo, and BDS satellites, respectively. The PESC positioning solutions can converge after a few minutes, and the RMS of PESC-SPP errors is 0.779 m, 0.609 m, and 2.060 m in E/N/U components, which is 32.2%, 24.1%, 32.0%, and 27.8%, 18.2%, 16.5% lower than that of SPP and PSC-SPP results, respectively.

Real-time GNSS precise point positioning with smartphones for vehicle navigation

The availability of raw Global Navigation Satellite System (GNSS) measurements from Android smart devices gives new possibilities for precise positioning solutions, e.g., Precise Point Positioning (PPP). However, the accuracy of the PPP with smart devices currently is a few meters due to the poor quality of the raw GNSS measurements in a kinematic scenario and in urban environments, particularly when the smart devices are placed inside vehicles. To promote the application of GNSS PPP for land vehicle navigation with smart devices, this contribution studies the real-time PPP with smartphones. For data quality analysis and positioning performance validation, two vehicle-based kinematic positioning tests were carried out using two Huawei Mate30 smartphones and two Huawei P40 smartphones with different installation modes: the vehicle-roof mode with smartphones mounted on the top roof outside the vehicle, and the dashboard mode with smartphones stabilized on the dashboard inside the vehicle. To realize high accuracy positioning, we proposed a real-time smartphone PPP method with the data processing strategies adapted for smart devices. Positioning results show that the real-time PPP can achieve the horizontal positioning accuracy of about 1–1.5 m in terms of root-mean-square and better than 2.5 m at the 95th percentile for the vehicle-based kinematic positioning with the experimental smartphones mounted on the dashboard inside the vehicle, which is the real scenario in vehicle navigation.

DGNSS Cooperative Positioning in Mobile Smart Devices: A Proof of Concept

Global Navigation Satellite System (GNSS) constitutes the foremost provider for geo-localization in a growing number of consumer-grade applications and services supporting urban mobility. Therefore, low-cost and ultra-low-cost, embedded GNSS receivers have become ubiquitous in mobile devices such as smartphones and consumer electronics to a large extent. However, limited sky visibility and multipath scattering induced in urban areas hinder positioning and navigation capabilities, thus threatening the quality of position estimates. This work leverages the availability of raw GNSS measurements in ultra-low-cost smartphone chipsets and the ubiquitous connectivity provided by modern, low-latency network infrastructures to enable a Cooperative Positioning (CP) framework. A Proof Of Concept is presented that aims at demonstratingdesigned to demonstrate the feasibility of a GNSS-only CP among networked smartphones embedding ultra-low-costequipped with GNSS receivers. The test campaign presented in this study assessed the feasibility of a client-serverthe approach over 4 G/LTE network connectivity. Results demonstrated an overall service availability above 80%, and an average accuracy improvement over the 40% w.r.t. to the GNSS standalone solution.

The Highest Peaks of the Mountains: Comparing the Use of GNSS, LiDAR Point Clouds, DTMs, Databases, Maps, and Historical Sources

Advances in remote data acquisition techniques have contributed to the flooding of society with spatial data sets and information. Widely available spatial data sets, including digital terrain models (DTMs) from aerial laser scanning (ALS) data, are finding more and more new applications. The article analyses and compares the heights of the 14 highest peaks of the Polish Carpathians derived from different data sources. Global navigation satellite system (GNSS) geodetic measurements were used as reference. The comparison primarily involves ALS data, and selected peaks’ GNSS measurements carried out with Xiaomi Mi 8 smartphones were also compared. Recorded raw smartphone GNSS measurements were used for calculations in post-processing mode. Other data sources were, among others, global and local databases and models and topographic maps (modern and old). The article presents an in-depth comparison of Polish and Slovak point clouds for two peaks. The results indicate the possible use of large-area laser scanning in determining the maximum heights of mountain peaks and the need to use geodetic GNSS measurements for selected peaks. For the Polish peak of Rysy, the incorrect classification of point clouds causes its height to be overestimated. The conclusions presented in the article can be used in the dissemination of knowledge and to improve positioning methods.

Integrated water vapour content retrievals from ship-borne GNSS receivers during EUREC<sup>4</sup>A

Abstract. In the framework of the EUREC4A (Elucidating the role of clouds–circulation coupling in climate) campaign that took place in January and February 2020, integrated water vapour (IWV) contents were retrieved over the open tropical Atlantic Ocean using Global Navigation Satellite System (GNSS) data acquired from three research vessels (R/Vs): R/V Atalante, R/V Maria S. Merian and R/V Meteor. This paper describes the GNSS processing method and compares the GNSS IWV retrievals with IWV estimates from the European Centre for Medium-range Weather Forecasts (ECMWF) fifth reanalysis (ERA5), from the Moderate Resolution Imaging Spectroradiometer (MODIS) infrared products and from terrestrial GNSS stations located along the tracks of the ships. The ship-borne GNSS IWV retrievals from R/V Atalante and R/V Meteor compare well with ERA5, with small biases (−1.62 kg m−2 for R/V Atalante and +0.65 kg m−2 for R/V Meteor) and a root mean square (rms) difference of about 2.3 kg m−2. The results for the R/V Maria S. Merian are found to be of poorer quality, with an rms difference of 6 kg m−2, which is very likely due to the location of the GNSS antenna on this R/V prone to multipath effects. The comparisons with ground-based GNSS data confirm these results. The comparisons of all three R/V IWV retrievals with MODIS infrared products show large rms differences of 5–7 kg m−2, reflecting the enhanced uncertainties in these satellite products in the tropics. These ship-borne IWV retrievals are intended to be used for the description and understanding of meteorological phenomena that occurred during the campaign, east of Barbados, Guyana and northern Brazil. Both the raw GNSS measurements and the IWV estimates are available through the AERIS data centre (https://en.aeris-data.fr/, last access: 20 September 2020). The digital object identifiers (DOIs) for R/V Atalante IWV and raw datasets are https://doi.org/10.25326/71 (Bosser et al., 2020a) and https://doi.org/10.25326/74 (Bosser et al., 2020d), respectively. The DOIs for the R/V Maria S. Merian IWV and raw datasets are https://doi.org/10.25326/72 (Bosser et al., 2020b) and https://doi.org/10.25326/75 (Bosser et al., 2020e), respectively. The DOIs for the R/V Meteor IWV and raw datasets are https://doi.org/10.25326/73 (Bosser et al., 2020c) and https://doi.org/10.25326/76 (Bosser et al., 2020f), respectively.

Sky visibility estimation based on GNSS satellite visibility: an approach of GNSS-based context awareness

Global navigation satellite system (GNSS) positioning in urban areas does not currently provide accurate and stable performance because surrounding buildings can block and reflect satellite signals. However, if we can determine the environment in which the receiver is located, appropriate positioning can be applied. For example, GNSS real-time kinematic and 3D-mapping-aided GNSS (3DMA GNSS) are used for positioning in open sky and urban areas, respectively. Thus, the context awareness of the GNSS receiver is important. In fact, an urban canyon can be further categorized into different levels based on sky visibility. We propose an innovative algorithm based on this categorization, which can provide information on surrounding buildings and give an estimation of sky visibility from raw GNSS measurements. This idea was inspired by the use of low-orbit satellite data for remote sensing applications. The recent development of multi-GNSS has led to a notable increase in the number of navigation satellites. Crucially, the visibility of satellites and the blockage of line-of-sight satellite signals are representative of the surrounding environment. The visibility of satellites can be classified by machine learning techniques, and an accurate classification can afford an estimation that is close to the real-sky visibility, as derived from a 3D building model and ground truth location. To assess the sensitivity of our proposed sky visibility estimation algorithm, we simulate different classification accuracies to investigate their effect on the performance of the algorithm.

A New DGNSS Positioning Infrastructure for Android Smartphones.

One’s position has become an important piece of information for our everyday lives in a smart city. Currently, a position can be obtained easily using smartphones that is equipped with low-cost Global Navigation Satellite System (GNSS) chipsets with accuracy varying from 5 m to 10 m. Differential GNSS (DGNSS) is an efficient technology that removes the majority of GNSS errors with the aid of reference stations installed at known locations. The sub-meter accuracy can be achieved when applying the DGNSS technology on the advanced receivers. In 2016, Android has opened the accesses of raw GNSS measurements to developers. However, most of the mid and low-end smartphones only provide the data using the National Marine Electronics Association (NMEA) protocol. They do not provide the raw measurements, and thus do not support the DGNSS operation either. We proposed a DGNSS infrastructure that correct the standalone GNSS position of smartphones using the corrections from the reference station. In the infrastructure, the position correction is generated considering the GNSS satellite IDs that contribute to the standalone solution in smartphones, and the position obtained is equivalent to the solution of using the range-domain correction directly. To serve a large number of smartphone users, a Client/Server architecture is developed to cope with a mass of DGNSS positioning requests efficiently. The comparison of the proposed infrastructure against the ground truth, for all field tests in open areas, showed that the infrastructure achieves the horizontal positioning accuracy better than 2 m. The improvement in accuracy can reach more than 50% for the test in the afternoon. The infrastructure brings benefits to applications that require more accuracy without requiring any hardware modifications.

Single-Baseline RTK Positioning Using Dual-Frequency GNSS Receivers Inside Smartphones.

Global Navigation Satellite System (GNSS) positioning is currently a common practice thanks to the development of mobile devices such as smartphones and tablets. The possibility to obtain raw GNSS measurements, such as pseudoranges and carrier-phase, from these instruments has opened new windows towards precise positioning using smart devices. This work aims to demonstrate the positioning performances in the case of a typical single-base Real-Time Kinematic (RTK) positioning while considering two different kinds of multi-frequency and multi-constellation master stations: a typical geodetic receiver and a smartphone device. The results have shown impressive performances in terms of precision in both cases: with a geodetic receiver as the master station, the reachable precisions are several mm for all 3D components while if a smartphone is used as the master station, the best results can be obtained considering the GPS+Galileo constellations, with a precision of about 2 cm both for 2D and Up components in the case of L1+L5 frequencies, or 3 cm for 2D components and 2 cm for the Up, in the case of an L1 frequency. Moreover, it has been demonstrated that it is not feasible to reach the phase ambiguities fixing: despite this, the precisions are still good and also the obtained 3D accuracies of positioning solutions are less than 1 m. So, it is possible to affirm that these results are very promising in the direction of cooperative positioning using smartphone devices.

Real-Time Geophysical Applications with Android GNSS Raw Measurements

The number of Android devices enabling access to raw GNSS (Global Navigation Satellite System) measurements is rapidly increasing, thanks to the dedicated Google APIs. In this study, the Xiaomi Mi8, the first GNSS dual-frequency smartphone embedded with the Broadcom BCM47755 GNSS chipset, was employed by leveraging the features of L5/E5a observations in addition to the traditional L1/E1 observations. The aim of this paper is to present two different smartphone applications in Geoscience, both based on the variometric approach and able to work in real time. In particular, tests using both VADASE (Variometric Approach for Displacement Analysis Stand-alone Engine) to retrieve the 3D velocity of a stand-alone receiver in real-time, and VARION (Variometric Approach for Real-Time Ionosphere Observations) algorithms, able to reconstruct real-time sTEC (slant total electron content) variations, were carried out. The results demonstrate the contribution that mass-market devices can offer to the geosciences. In detail, the noise level obtained with VADASE in a static scenario—few mm/s for the horizontal components and around 1 cm/s for the vertical component—underlines the possibility, confirmed from kinematic tests, of detecting fast movements such as periodic oscillations caused by earthquakes. VARION results indicate that the noise level can be brought back to that of geodetic receivers, making the Xiaomi Mi8 suitable for real-time ionosphere monitoring.

Cooperative Position Prediction: Beyond Vehicle-to-Vehicle Relative Positioning

Reliable and accurate relative position prediction techniques are vital to the integrity of cooperative intelligent transportation systems (C-ITS) because most C-ITS safety applications should constantly access a (relative-) localization mechanism that satisfies not only a certain (relative-) position accuracy range but also a minimal computational complexity. A global navigation satellite system (GNSS) coupled with dedicated short-range communications (DSRC) links is a candidate for the localization system required for vehicular relative positioning. The vehicle-to-vehicle (V2V) exchange of safety-related data such as raw GNSS measurements, as accurate and frequent as possible, via DSRC links allows the vehicular localization mechanism to maintain real-time relative positioning (RRP) vectors. However, V2V DSRC safety messages may include inaccurate positioning data or may fast become outdated, and/or DSRC links may occasionally fail due to various adverse scenarios including network overheads that drop the bandwidth of vehicular ad-hoc networks. Hence, each DSRC-enabled vehicle may not necessarily know the latest positioning data of neighboring vehicles. This calls upon reliable and accurate relative position prediction mechanisms to cover the DSRC-related deficiencies of relative positioning. This paper, at first, studies the benefits of a terrestrial communications system complementing DSRC-based V2V RRP and then considers position prediction techniques suitable for V2V RRP and examines a few existing solutions and makes a detailed comparison based on a set of required positioning performance parameters for vehicle safety application.

An Enhanced RAIM Method for Satellite-Based Positioning Using Track Constraint

Integrity of global navigation satellite system (GNSS) positioning is one major concern for the GNSS-based railway train control systems due to critical safety requirements. In this paper, we propose a map-enhanced receiver autonomous integrity monitoring (RAIM) method for satellite-based positioning of railway trains, which could benefit from the autonomy of fault detection and exclusion (FDE) without relying on additional sensors in a location determination system (LDS). With the proposed RAIM solution, the spatial constraint from the fixed railway tracks is employed to predict the measurements from invisible satellites, which makes it possible of performing FDE even when insufficient satellites are visible to enable the conventional RAIM operation. We demonstrate that different faults in raw GNSS measurements can be identified and isolated by extracting immediate information from the state estimator. With a virtual satellite pseudorange generation mechanism, two-stage FDE architecture is proposed. A pseudorange consistency check logic in the measurement domain is adopted at a local stage. Furthermore, a filter-innovation-based RAIM strategy is utilized to realize a global test in the positioning domain under a cubature Kalman filter (CKF)-based sensor data fusion framework. The proposed method is verified by the field experiment and simulations and the results show the improvement to the availability and safety of GNSS-based positioning.

Environmental Context Detection for Adaptive Navigation using GNSS Measurements from a Smartphone

The signals available for navigation depend on the environment. To operate reliably in a wide range of different environments, a navigation system is required to adopt different techniques based on the environmental contexts. In this paper, an environmental context detection framework is proposed, building the foundation of a context adaptive navigation system. Different land environments are categorized into indoor, urban, and open-sky environments based on how Global Navigation Satellite System (GNSS) positioning performs in these environments. Indoor and outdoor environments are first detected based on the availability and strength of GNSS signals using a hidden Markov model. Then the further classification of outdoor environments into urban and open-sky is investigated. Pseudorange residuals are extracted from raw GNSS measurements in a smartphone and used for classification in a fuzzy inference system alongside the signal strength data. Practical test results under different kinds of environments demonstrate an overall 88.2 percent detection accuracy.