Raw Global Navigation Satellite System Research Articles

New Concept of Smart UAS-GCP: A Tool for Precise Positioning in Remote-Sensing Applications

Today, ground control points (GCPs) represent indispensable tools for products’ georeferencing in all the techniques concerning remote sensing (RS), particularly in monitoring activities from unmanned aircraft system (UAS) platforms. This work introduces an innovative tool, smart GCPs, which combines different georeferencing procedures, offering a range of advantages. It can serve three fundamental purposes concurrently: (1) as a drone takeoff platform; (2) as a base station, allowing the acquisition of raw global navigation satellite system (GNSS) data for post-processed kinematic (PPK) surveys or by providing real-time GNSS corrections for precision positioning; (3) as a rover in the network real-time kinematic (NRTK) mode, establishing its position in real time with centimetric precision. The prototype has undergone testing in a dedicated study area, yielding good results for all three geodetic correction techniques: PPK, RTK, and GCP, achieving centimeter-level accuracy. Nowadays, this versatile prototype represents a unique external instrument, which is also easily transportable and able to connect to the GNSS RING network, obtaining real-time positioning corrections for a wide range of applications that require precise positioning. This capability is essential for environmental applications that require a multitemporal UAS-based study. When the real-time RING data are accessible to the scientific community operating in RS surveying, this work could be a helpful guide for researchers approaching such investigations.

T-SPP: Improving GNSS Single-Point Positioning Performance Using Transformer-Based Correction

GNSS (global navigation satellite systems) technology enables high-precision single-point positioning (SPP) in open environments. However, the accuracy of GNSS positioning is significantly compromised in complex urban canyons due to signal obstructions and non-line-of-sight propagation errors. To address this challenge, we propose a GNSS displacement estimation algorithm. This method learns nonlinear dependencies between GNSS raw measurements and corresponding position changes, capturing dynamic and layered features in GNSS measurement data for displacement estimation. We introduce a denoising auto-encoder (DAE) to preprocess raw GNSS observations, reducing the impact of noise. The model simultaneously outputs estimated displacement and model confidence. The fusion process dynamically combines positioning results from the SPP algorithm and the D-Tran model, adaptively blending them to achieve accurate and optimal positioning estimation. This approach optimizes the accuracy of estimated positioning results while maintaining confidence in the estimation. Experimental results show a 61% reduction in root mean square error (RMSE) and 100% availability in urban canyon environments compared to traditional single-point positioning techniques.

Investigation of Ionospheric Small‐Scale Plasma Structures Associated With Particle Precipitation

AbstractWe investigate the role of auroral particle precipitation in small‐scale (below hundreds of meters) plasma structuring in the auroral ionosphere over the Arctic. In this scope, we analyze together data recorded by an Ionospheric Scintillation Monitor Receiver (ISMR) of Global Navigation Satellite System (GNSS) signals and by an All‐Sky Imager located in Longyearbyen, Svalbard (Norway). We leverage on the raw GNSS samples provided at 50 Hz by the ISMR to evaluate amplitude and phase scintillation indices at 1 s time resolution and the Ionosphere‐Free Linear Combination at 20 ms time resolution. The simultaneous use of the 1 s GNSS‐based scintillation indices allows identifying the scale size of the irregularities involved in plasma structuring in the range of small (up to few hundreds of meters) and medium‐scale size ranges (up to few kilometers) for GNSS frequencies and observational geometry. Additionally, they allow identifying the diffractive and refractive nature of fluctuations on the recorded GNSS signals. Six strong auroral events and their effects on plasma structuring are studied. Plasma structuring down to scales of hundreds of meters is seen when strong gradients in auroral emissions at 557.7 nm cross the line of sight between the GNSS satellite and receiver. Local magnetic field measurements confirm small‐scale structuring processes coinciding with intensification of ionospheric currents. Since 557.7 nm emissions primarily originate from the ionospheric E‐region, plasma instabilities from particle precipitation at E‐region altitudes are considered to be responsible for the signatures of small‐scale plasma structuring highlighted in the GNSS scintillation data.

Towards Smarter Positioning through Analyzing Raw GNSS and Multi-Sensor Data from Android Devices: A Dataset and an Open-Source Application

The state-of-the-art Android environment, available on a major market share of smartphones, provides an open playground for sensor data gathering. Moreover, the rise in new types of devices (e.g., wearables/smartwatches) is further extending the market opportunities with a variety of new sensor types. The existing implementations of biometric/medical sensors can allow the general public to directly access their health measurements, such as Electrocardiogram (ECG) or Oxygen Saturation (SpO2). This access greatly increases the possible applications of these devices with the combination of all the onboard sensors that are broadly in use nowadays. In this study, we look beyond the current state of the art into the positioning capacities of Android smart devices and wearables, with a focus on raw Global Navigation Satellite Systems (GNSS) measurements that are still mostly lacking in the research world. We develop a novel open-source Android application working in both smartphone and smartwatch environments for multi-sensor measurement data logging that also includes GNSS, an Inertial Navigation System (INS) magnetometer, and a barometer. Four smartphones and one smartwatch are used to perform surveys in different scenarios. The extraction of GNSS raw data from a wearable device has not been reported yet in the literature and no open-source app has existed so far for extracting GNSS data from wearables. Not only the developed app but also the results of these measurement surveys are provided as an open-access dataset. We start by defining our methodology and the acquisition protocol, and we dive into the structure of the dataset files. We also propose a first analysis of the data logged and evaluate the data according to several performance metrics. A discussion reviewing the capacities of smart devices for advanced positioning is proposed, as well as the current open challenges.

Assessment of GNSS observations and positioning performance from non-flagship Android smartphones

Abstract Android smartphone has gained attention in precise positioning applications since it can collect raw observable GNSS (Global Navigation Satellite System) data. Some studies have reported that the positioning accuracy may reach the sub-decimeter level. However, these studies mostly rely on a flagship Android smartphone that is made with better internal hardware, while the use of a non-flagship Android smartphone is not reported for this field. In this study, therefore, we explore non-flagship Android smartphones for positioning applications. We assessed the observable data quality and positioning performance of two non-flagship Android GNSS smartphones of a Samsung M21 and a Redmi Note 7. The data quality assessment includes satellite tracking and carrier-to-noise density ratio analysis. Also, the positioning performance was assessed for Single Point Positioning (SPP) and relative positioning methods in static and open-sky conditions. In addition, the residual properties of GNSS measurements were also evaluated. The results were further compared to the high-grade GNSS device. We found that the observable pseudorange and carrier phase measurements from Android smartphones were about 70 % and 36 % of what high-grade GNSS obtained. Furthermore, within a span of 1 h of observations, a considerable amount of cycle slips, amounting to as many as 518 instances, were noted in the observations from Android GNSS devices. While for the carrier-to-noise density ratio in Android smartphones, it was estimated to be about 15 dB-Hz lower than in high-grade GNSS devices. The spread of the residuals for pseudorange and carrier phase from Android smartphones was estimated to be about ±15 and ±6 m, respectively. The 3D positioning error for SPP was estimated to be about 4.7 m, with a position spread reaching tens of meters. At the same time, the 3D positioning error was calculated to be 4.6 m with the estimated standard error at the centimeter level when using the relative positioning method. To improve the positioning performance, applying a C/N0 mask to the observations become the best solution. The 3D positioning error for the relative positioning method reduces to 2.7 m when applying a C/N0 mask of 30 dB-Hz. The observable data quality of non-flagship Android GNSS devices possibly causes relatively poor performance of positioning applications.

Precise point positioning with mixed single- and dual-frequency GNSS observations from Android smartphones considering code-carrier inconsistency

The open access to raw Global Navigation Satellite System (GNSS) measurements from Android smartphones brings new possibilities to high-accuracy positioning with precise point positioning (PPP) technique. However, decimeter or even centimeter level positioning accuracy is still challengeable mainly due to the low completeness of dual-frequency observations and specific observation errors from a smartphone. Huawei P40 released in 2020 is the first module that can track multi-GNSS multi-frequency signals. Based on GNSS observations from P40 and a co-located geodetic-grade receiver, a method is proposed to extract and model the P40 code-carrier inconsistency errors. The P40 observation inconsistency presents a linear drift pattern with respect to time at each frequency and can even reach hundreds of meters. A correction model is then established which reduces the code-carrier differences to a level of few meters. Regarding to the P40 code-carrier inconsistency error signature, an uncombined precise point positioning (PPP) algorithm is developed with the inconsistency error treated with different schemes, including correction using established model, parameterization as a linear function, or estimated as an independent clock offset for carrier-phase observation. Meanwhile, to cope with the dual-frequency data deficiency of P40, the PPP algorithm is designed based on pure single-frequency processing which can handle mixed single- and dual-frequency observations. Positioning performances of three schemes are validated in both static and kinematic scenarios. With applying the code-carrier inconsistency schemes, P40 positioning precision reaches 0.2 m and 1.0 m for static and kinematic experiments, respectively, which is improved by an order of magnitude against the case without. Furthermore, precision can be even improved when P40 is equipped with an external geodetic-grade antenna. It reaches 5.8 and 18.2 cm in the static and kinematic tests, respectively, revealing improvements of 28.7% and 54.4% against the case without considering the inconsistencies, respectively. Such result is very encouraging and indicate a positioning accuracy level close to a geodetic receiver.

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.

Method for Assemblage of an Open Access Data Set for Research in Geomagnetic Effects on GPS/GNSS Ionospheric Delay in Sub-equatorial Regions

Space weather, geomagnetic, and ionospheric effects are major sources of Global Navigation Satellite System’s (GNSS) Positioning, Navigation, and Timing (PNT) service degradation. Researchers studying the problem often face an obstacle in missing large data sets comprising ionospheric effects variables and space weather ones for their studies. Here a formal assemblage method is proposed for data set assemblage for GNSS ionospheric effects assessment that utilises experimental sets of space weather/geomagnetic observations, and raw GNSS pseudorange observations polluted with uncorrected ionospheric delay effects to estimate Total Electron Content (TEC) as a target variable outlining the ionospheric effects on GNSS. The five Observations Quality Principles (OQP) are proposed for the observations to be used in statistical learning model development to comply to. The proposed assemblage method is demonstrated in a particular case study of an assembled data set for Darwin, NT, Australia, in sub-equatorial zone, using a tailored software developed in the R environment for scientific computing, and a freely available TEC estimation software. The assembled massive data obtained in the demonstration of the proposed method is provided in the open-access manner in support of the international scientific community.

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.

InGVIO: A Consistent Invariant Filter for Fast and High-Accuracy GNSS-Visual-Inertial Odometry

Combining Global Navigation Satellite System (GNSS) with visual and inertial sensors can give smooth pose estimation without drifting. The fusion system gradually degrades to Visual-Inertial Odometry (VIO) with the number of satellites decreasing, which guarantees robust global navigation in GNSS unfriendly environments. In this letter, we propose an open-sourced invariant filter-based platform, InGVIO, to tightly fuse monocular/stereo visual-inertial measurements, along with raw data from GNSS. InGVIO gives highly competitive results in terms of computational load compared to current graph-based algorithms, meanwhile possessing the same or even better level of accuracy. Thanks to our proposed marginalization strategies, the baseline for triangulation is large although only a few cloned poses are kept. Moreover, we define the infinitesimal symmetries of the system and exploit the various structures of its symmetry group, being different from the total symmetries of the VIO case, which elegantly gives results for the pattern of degenerate motions and the structure of unobservable subspaces. We prove that the properly-chosen invariant error is still compatible with all possible symmetry group structures of InGVIO and has intrinsic consistency properties. Besides, InGVIO has strictly linear error propagation without linearization error. InGVIO is tested on both open datasets and our proposed fixed-wing datasets with variable levels of difficulty and various numbers of satellites. The latter datasets, to the best of our knowledge, are the first datasets open-sourced to the community on a fixed-wing aircraft with raw GNSS.

A Comprehensive Analysis of Smartphone GNSS Range Errors in Realistic Environments.

Precise positioning using smartphones has been a topic of interest especially after Google decided to provide raw GNSS measurement through their Android platform. Currently, the greatest limitations in precise positioning with smartphone Global Navigation Satellite System (GNSS) sensors are the quality and availability of satellite-to-smartphone ranging measurements. Many papers have assessed the quality of GNSS pseudorange and carrier-phase measurements in various environments. In addition, there is growing research in the inclusion of a priori information to model signal blockage, multipath, etc. In this contribution, numerical estimation of actual range errors in smartphone GNSS precise positioning in realistic environments is performed using a geodetic receiver as a reference. The range errors are analyzed under various environments and by placing smartphones on car dashboards and roofs. The distribution of range errors and their correlation to prefit residuals is studied in detail. In addition, a comparison of range errors between different constellations is provided, aiming to provide insight into the quantitative understanding of measurement behavior. This information can be used to further improve measurement quality control, and optimize stochastic modeling and position estimation processes.

Precise Position Estimation Using Smartphone Raw GNSS Data Based on Two-Step Optimization.

This paper presents a high-precision positioning method using raw global navigation satellite system (GNSS) observations from smartphones in the Google smartphone decimeter challenge (GSDC). Compared to commercial GNSS receivers, smartphone GNSS observations are noisy owing to antenna limitations, making it difficult to apply conventional high-precision positioning methods. In addition, it is important to exclude outliers in GSDC because GSDC includes data in environments where GNSS is shielded, such as tunnels and elevated structures. Therefore, this study proposes a smartphone positioning method based on a two-step optimization method, using factor graph optimization (FGO). Here, the velocity and position optimization process are separated and the velocity is first estimated from Doppler observations. Then, the outliers of the velocity estimated by FGO are excluded, while the missing velocity is interpolated. In the next position-optimization step, the velocity estimated in the previous step is adopted as a loose state-to-state constraint and the position is estimated using the time-differenced carrier phase (TDCP), which is more accurate than Doppler, but less available. The final horizontal positioning accuracy was 1.229 m, which was the first place, thus demonstrating the effectiveness of the proposed method.

Targetless Extrinsic Calibration of LiDAR–IMU System Using Raw GNSS Observations for Vehicle Applications

Nowadays, the low-cost MEMS-based Inertial Measurement Units (IMU) and LiDARs are commercially available in vehicle systems and are usually integrated to achieve robust ego-motion estimation due to their excellent complementary properties. In this case, accurate inter-sensor spatial transformation, i.e. extrinsic parameters, is a fundamental prerequisite for the combined application of LiDAR and IMU. However, current LiDAR-IMU calibration methods usually rely on specially-designed artificial targets or facilities, which greatly limits the flexibility and usability of calibration. Fortunately, modern intelligent vehicles are generally equipped with portable global navigation satellite system (GNSS) devices, which are able to provide precise positioning services and could be used to improve the performance of extrinsic calibration. To this end, we innovatively introduce raw GNSS observations to enhance in-vehicle LiDAR-IMU calibration performance without predefined target support. Based on the centralized Extended Kalman Filter (EKF), the estimator tightly fuses the double-differenced pseudorange and carrier phase observations, IMU data, and extracted plane features to obtain the optimal extrinsic parameters. The results of simulated tests and real-world experiments indicate that the introduction of GNSS can significantly improve the calibration performance. The GNSS-aided LiDAR-IMU calibration results also show better odometry accuracy and mapping consistency than those without GNSS aiding.

Resilient Smartphone Positioning Using Native Sensors and PPP Augmentation

<h3>Abstract</h3> With the ubiquitous use of global navigation satellite system (GNSS) receivers, navigation solutions from smartphones have become integrated in various applications throughout our lives. These ultra-low-cost GNSS receivers have the drawbacks of insufficient observations and poorer signal reception quality than higher-cost receivers. Since 2016, smartphones using the Android operating system have been able to output raw GNSS pseudorange and carrier-phase measurements, thereby enabling improved navigation capabilities. The realm of sensor fusion is also being explored by using smartphone sensors, including inertial measurement units (IMUs), cameras, and other fusion techniques. The research presented herein deployed only IMU and GNSS sensors native to existing smartphones and achieved a standalone solution using PPP/IMU integration that outperformed standard techniques. In open-sky vehicle experiments, the sensor integration algorithm achieved 1.6-m horizontal RMS, thus reducing 80% of horizontal errors in GNSS-challenging environments through a tightly coupled GNSS-PPP solution that is yet to appear in publications.

BitTorrent is Apt for Geophysical Data Collection and Distribution

This article covers a nouveau idea of a way to collect and handle geophysical data with a peer-to-peer network in near real-time. The text covers a brief introduction to the cause, the technology, and the particular case of collecting data from Global Navigation Satellite System (GNSS) stations. We describe the proof-of-concept implementation that has been tested. The test was conducted with an experimental GNSS station and a data aggregation facility. In the test, original raw GNSS signal measurements were transferred to the data aggregation center and subsequently to the consumer. Our implementation utilized the BitTorrent protocol to communicate and transfer data. The solution could be used to enable the majority of data aggregation centers for fast, reliable, and transparent real-time data handling experience for the benefit of the scientific community.

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.

In situ observations of the Swiss periglacial environment using GNSS instruments

Abstract. Monitoring of the periglacial environment is relevant for many disciplines including glaciology, natural hazard management, geomorphology, and geodesy. Since October 2022, Rock Glacier Velocity (RGV) is a new Essential Climate Variable (ECV) product within the Global Climate Observing System (GCOS). However, geodetic surveys at high elevation remain very challenging due to environmental and logistical reasons. During the past decades, the introduction of low-cost global navigation satellite system (GNSS) technologies has allowed us to increase the accuracy and frequency of the observations. Today, permanent GNSS instruments enable continuous surface displacement observations at millimetre accuracy with a sub-daily resolution. In this paper, we describe decennial time series of GNSS observables as well as accompanying meteorological data. The observations comprise 54 positions located on different periglacial landforms (rock glaciers, landslides, and steep rock walls) at altitudes ranging from 2304 to 4003 ma.s.l. and spread across the Swiss Alps. The primary data products consist of raw GNSS observables in RINEX format, inclinometers, and weather station data. Additionally, cleaned and aggregated time series of the primary data products are provided, including daily GNSS positions derived through two independent processing tool chains. The observations documented here extend beyond the dataset presented in the paper and are currently continued with the intention of long-term monitoring. An annual update of the dataset, available at https://doi.org/10.1594/PANGAEA.948334 (Beutel et al., 2022),​​​​​​​ is planned. With its future continuation, the dataset holds potential for advancing fundamental process understanding and for the development of applied methods in support of e.g. natural hazard management.