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The Radio & Plasma Wave Investigation (RPWI) onboard the ESA JUpiter ICy moons Explorer (JUICE) is described in detail. The RPWI provides an elaborate set of state-of-the-art electromagnetic fields and cold plasma instrumentation, including active sounding with the mutual impedance and Langmuir probe sweep techniques, where several different types of sensors will sample the thermal plasma properties, including electron and ion densities, electron temperature, plasma drift speed, the near DC electric fields, and electric and magnetic signals from various types of phenomena, e.g., radio and plasma waves, electrostatic acceleration structures, induction fields etc. A full wave vector, waveform, polarization, and Poynting flux determination will be achieved. RPWI will enable characterization of the Jovian radio emissions (including goniopolarimetry) up to 45 MHz, has the capability to carry out passive radio sounding of the ionospheric densities of icy moons and employ passive sub-surface radar measurements of the icy crust of these moons. RPWI can also detect micrometeorite impacts, estimate dust charging, monitor the spacecraft potential as well as the integrated EUV flux. The sensors consist of four 10 cm diameter Langmuir probes each mounted on the tip of 3 m long booms, a triaxial search coil magnetometer and a triaxial radio antenna system both mounted on the 10.6 m long MAG boom, each with radiation resistant pre-amplifiers near the sensors. There are three receiver boards, two Digital Processing Units (DPU) and two Low Voltage Power Supply (LVPS) boards in a box within a radiation vault at the centre of the JUICE spacecraft. Together, the integrated RPWI system can carry out an ambitious planetary science investigation in and around the Galilean icy moons and the Jovian space environment. Some of the most important science objectives and instrument capabilities are described here. RPWI focuses, apart from cold plasma studies, on the understanding of how, through electrodynamic and electromagnetic coupling, the momentum and energy transfer occur with the icy Galilean moons, their surfaces and salty conductive sub-surface oceans. The RPWI instrument is planned to be operational during most of the JUICE mission, during the cruise phase, in the Jovian magnetosphere, during the icy moon flybys, and in particular Ganymede orbit, and may deliver data from the near surface during the final crash orbit.

Although GNSS (Global Navigation Satellite System) is well-established for outdoor positioning, it still encounters challenges in urban occluded environments. Currently, multi-source fusion positioning has emerged as the primary solution. Since commonly used smartphones can simultaneously receive satellite signals and send 5G signals, researching GNSS/5G fusion positioning based on smartphones is a highly feasible solution. However, existing studies on GNSS/5G fusion positioning primarily rely on simulation data and TOA (Time of Arrival). On the one hand, simulation data often fail to accurately reflect positioning performance in real-world environments. On the other hand, while TOA often struggles to achieve high accuracy due to time synchronization errors, the AOA (Angle of Arrival) method, which does not depend on time synchronization, presents a promising alternative. Therefore, we propose a GNSS/5G tightly coupled fusion positioning method based on AOA measurements and conduct practical tests. For the first time, we use a smartphone to verify the performance of this method in urban occluded environments. The static experimental results indicate that SPP of the smartphone performs poorly in occluded environments. In contrast, AOA positioning demonstrates relatively stable performance. GNSS/5G fusion positioning yields the best positioning results, exhibiting a best improvement of 98.18% over SPP and 70.69% over AOA positioning. For the two dynamic routes with varying levels of occlusion, GNSS/5G fusion positioning shows considerable enhancements, achieving improvements of 39.39% and 9.32% over SPP, and 13.35% and 44.68% over AOA positioning. These results demonstrate that the fusion positioning method can effectively compensate for the shortcomings of satellite positioning in occluded environment.

Vertical land motion and the redistribution of masses within and on the surface of the Earth affect the Earth’s gravity field. Hence, studying the ratio between temporal changes of the surface gravity \(\left( {\dot{g}} \right)\) and height ( \(\dot{h}\) ) is important in geoscience, e.g., for reduction of gravity observations, assessing satellite gravimetry missions, and tuning vertical land motion models. Sjöberg and Bagherbandi (2020) estimated a combined ratio of \(\dot{g}/\dot{h}\) in Fennoscandia based on relative gravity observations along the 63 degree gravity line running from Vågstranda in Norway to Joensuu in Finland, 688 absolute gravity observations observed at 59 stations over Fennoscandia, monthly gravity data derived from the GRACE satellite mission between January 2003 and August 2016, as well as a land uplift model. The weighted least-squares solution of all these data was \(\dot{g}/\dot{h}\)  =  − 0.166 ± 0.011 μGal/mm, which corresponds to an upper mantle density of about 3402 ± 95 kg/m3. The present note includes additional GRACE data to June 2017 and GRACE Follow-on data from June 2018 to November 2023. The resulting weighted least-squares solution for all data is \(\dot{g}/\dot{h}\)  =  − 0.160 ± 0.011 μGal/mm, yielding an upper mantle density of about 3546 ± 71 kg/m3. The outcomes show the importance of satellite gravimetry data in Glacial Isostatic Adjustment (GIA) modeling and other parameters such as land uplift rate. Utilizing a longer time span of GRACE and GRACE Follow-on data allows us to capture fine variations and trends in the gravity-to-height ratio with better precision. This will be useful for constraining and adjusting GIA models and refining gravity observations.

Ionospheric scintillations disrupt the trans-ionospheric satellite signals and cause quandaries in satellite applications typically near the low equatorial sites; GNSS signals are utilized extensively for monitoring such anomalies. This work presents the unique results that confirm the suitability and limitations of a commercial low-cost, GNSS module (Ublox ZED F9P) for amplitude scintillation monitoring from a location in India situated near the EIA crest during the autumnal equinox of 2022 for low to intense amplitude scintillations. Comparison of amplitude scintillation index (S4) and fade rate using concurrent data from a Leica GR50 geodetic receiver and the low-cost module shows fairly good agreement between the results. The findings have practical utility in designing cost, size, and power-efficient GNSS probes using such modules for ionospheric research. Such modules are not a replacement for the traditional receivers but can be utilized to implement a multi-point, autonomous amplitude scintillation monitoring network.

LARES-2 is a new geodetic satellite designed for high-accuracy satellite laser ranging. The orbit altitude of LARES-2 is similar to that of LAGEOS-1, whereas the inclination angle of 70° complements the LAGEOS-1 inclination of 110°; hence, both satellites form the butterfly configuration for the verification of the Lense–Thirring effect. Although the major objective of LARES-2 is testing general relativity, LARES-2 substantially contributes to geodesy in terms of the realization of terrestrial reference frames, recovery of the geocenter motion, pole coordinates, length-of-day, and low-degree gravity field coefficients. We analyze the first 1.5 years of LARES-2 data and test different empirical orbit models for LARES-2 with and without co-estimating low-degree gravity field coefficients to find the best combination strategy with LAGEOS satellites. We found that LARES-2 orbit determination is more accurate than that of LAGEOS-1/2 due to a different satellite construction consisting of a solid sphere with no inner structure. Neither the correction for D0 nor the empirical once-per-revolution along-track accelerations SC/SS have to be estimated for LARES-2 when co-estimating gravity field coefficients. The only empirical parameter needed for LARES-2 is the constant along-track acceleration S0 to compensate for the Yarkovsky–Schach effect. On the contrary, for LAGEOS-1/2, the non-gravitational perturbations affect C30 and Z geocenter estimates when once-per-revolution parameters are not estimated. LARES-2 does not face this issue. LARES-2 improves the formal errors of the Z geocenter component by up to 59% and C20 by up to 40% compared to the combined LAGEOS-1/2 solutions and provides C30 estimates unaffected by thermal orbit modeling issues.

An Erratum to this paper has been published: https://doi.org/10.1134/S1064226924550023

An Erratum to this paper has been published: https://doi.org/10.1134/S106422692456002X

An Erratum to this paper has been published: https://doi.org/10.1134/S1064226924570014

An Erratum to this paper has been published: https://doi.org/10.1134/S1064226924560018

An Erratum to this paper has been published: https://doi.org/10.1134/S1064226924550011

Smartphone-based location-based services (LBS) require enhanced horizontal position accuracy with integrity. Due to the mass-market nature and compact design of smartphones, they utilize low-cost antennas and receivers, making them susceptible to multipath effects and other errors, which complicates the differentiation between reliable and unreliable measurements. To address these challenges, this paper explores the application of an adaptive Kalman filter technique to improve smartphone positioning accuracy. Adaptive Kalman filters adjust parameters such as process noise covariance or measurement noise covariance to modify the filter gain. When augmented with outlier detection mechanisms, the filter becomes more robust. This paper introduces a robust adaptive Kalman filter to enhance smartphone position accuracy. Outliers are detected using standardized innovations as a learning statistic, and a t-test is applied to these statistics to identify and mitigate outliers and adapt the measurement noise covariance accordingly. While previous research used empirical values for thresholds to adapt measurement noise covariance matrix, this study derives thresholds from t-tests, contingent on the normal distribution of learning statistics. By eliminating clock reset effects, innovations are transformed from bimodal to a normal distribution. Testing across multiple datasets demonstrates reductions of up to 42% in horizontal positioning root mean square error, with 50th, 68th, and 95th percentile statistics showing improvements of up to 53%, 41%, and 61%, respectively.

Groundwater withdrawal and recharge lead to changes in terrestrial hydrological loads, which in turn cause surface deformation. Based on poroelastic response and elastic loading theory, the 24 Global Positioning System (GPS) stations on the North China Plain (NCP) and the Gravity Recovery and Climate Experiment mission and its follow-on (GRACE/GRACE-FO) are first integrated to quantify the spatial–temporal changes in surface deformation and groundwater storage (GWS) during 2011–2022. The results show that the trends of GWS in the three periods of 2011–2017, 2018–2020, and 2021–2022 were  − 2.56 ± 0.33 mm/yr,  − 4.72 ± 1.74 mm/yr, and 11.76 ± 4.18 mm/yr, respectively. Most of the GPS stations showed a significant negative correlation between GWS and surface deformation under the elastic loading theory. In 2021, surface subsidence of more than 5 mm was experienced by 94% of the GPS stations, and 58% experienced more than 10 mm, further confirming that the South-to-North Water Diversion (SNWD) effectively replenishes groundwater resources in the NCP. The SNWD, precipitation, and human activity were the three principal factors influencing the groundwater in the NCP. SNWD effectively mitigated the continuous decrease of groundwater in the NCP.

Satellite signals from the Global Navigation Satellite System (GNSS) are refracted as they pass through the troposphere, owing to the variable density and composition of the atmosphere, causing tropospheric delay. Typically, tropospheric delay is treated as an unknown parameter in GNSS data processing. Given the growing need for real-time GNSS applications, accurate tropospheric delay predictions are crucial to improve Precise Point Positioning (PPP). In this paper, time-series of tomography data are used for wet refractivity prediction employing Machine Learning (ML) techniques in both Poland and California, under extreme weather conditions including sweeping rain bands and storms. The predicted wet refractivity is implemented for tropospheric delay determination through ray-tracing technique. PPP processing is conducted in both static and kinematic modes using different setups. These are: (1) common PPP, called Com-PPP, (2) Ray-PPP, which applies obtained tropospheric delay on GNSS observations and thus eliminates tropospheric parameters from unknowns, and (3) Dif-PPP, which applies the difference of estimated tropospheric delay from ray-tracing and GNSS measurements to compensate for the remaining tropospheric delay in the observations. The results show that Dif-PPP reduces the Mean Absolute Error (MAE) of the Three-Dimensional (3-D) component between 8 and 33% in static mode compared to the Com-PPP method. Additionally, it can improve the convergence time of the up component in the kinematic mode by between 6 and 17%.

For precise orbit determination (POD) and precise applications with POD products, one of the critical issues is the modeling of non-conservative forces acting on satellites. Since the official publication of Galileo satellite metadata in 2017, analytical models including the box-wing model and thermal thrust models have been established to absorb a substantial amount of solar radiation pressure (SRP) and thermal thrust. These models serve as the foundation for the best overall modeling approach, combining the analytical box-wing model and thermal thrust model with parameterization of the remaining non-conservative perturbing forces using various optimized Empirical CODE Orbit Models (ECOMs) of the Center for Orbit Determination in Europe (CODE). Firstly, we have demonstrated the significance of the second-order signals in the D direction and the first-order signals in the B direction through spectral analyses of the pure box-wing model, which are consistent with the currently recommended 7-parameter Empirical CODE Orbit Model 2 (ECOM2). In spite of this, we still found that degradation in orbit accuracy frequently occurs during deep eclipse seasons when using the ECOM2 model. We confirm a high-frequency signal existing in the fluctuating orbit overlap differences through the spectral analysis. Considering this, the ECOM2 force model should be extended to higher order and adapted to absorb the remaining effects of potential perturbing forces. After extending the ECOM2 force model to the sixth order in the Sun direction, we demonstrated the significance of fourth- and sixth-order sine terms for deep eclipses. Due to the higher-order periodic terms, the averaged RMS values of orbit overlap difference over deep eclipses can be reduced from 5.3, 10.8, and 23.8 cm to 3.2, 3.9, and 9.9 cm for in-orbit validation (IOV) satellites, from 5.0, 8.6, and 17.7 cm to 3.0, 3.0, and 7.1 cm for the first generation of full operational capability (FOC-1) satellites, and from 5.4, 8.6, and 19.0 cm to 3.6, 3.6, and 7.4 cm for the second generation of FOC (FOC-2) satellites, in the radial, cross-track, and along-track directions, respectively. Fluctuations with a peak amplitude of approximately 0.4 nm/s2 in the bias in the solar panel axis (Y) direction (Y-bias) are effectively mitigated by the higher-order terms. Due to the higher-order terms, the vertical positioning errors during kinematic precise point positioning (PPP) convergence can be improved from 42.3 to 37.1 cm at the 95.5% confidence level. Meanwhile, a low correlation level of up to 0.02 is found between the newly introduced higher-order parameters and earth rotation parameters (ERPs).

The European Galileo High Accuracy Service (HAS) started to provide freely and openly accessible real-time precise satellite orbit, clock and code bias products to global users on January 24, 2023. Combined with the already running BeiDou PPP-B2b service, the launch of a variety of satellite-based PPP services provided more choices to users. However, different satellite-based PPP services provide services for different GNSS systems, which hamper users to make full use of multi-GNSS systems. Therefore, the combination of different satellite-based products can further improve the availability of corrections, usage of multi-GNSS observation data and positioning performance. This paper proposes to combine HAS and PPP-B2b products by the Helmert coordinate transformation method. To validate the algorithm, HAS and PPP-B2b products of day of year (DOY) 308–317 in 2023 were collected in Zhengzhou, China. First, they are evaluated in terms of correction availability, orbit and clock quality. Then the HAS and PPP-B2b products are combined by the Helmert coordinate transformation method. Two combination strategies are proposed. The first strategy is integrating BDS satellites of PPP-B2b products into HAS products (denoted as C_H), while the second strategy is integrating Galileo satellites of HAS products into PPP-B2b products (denoted as C_B). Finally, the combined strategies are evaluated with static and kinematic data. Based on the static data of 18 Multi-GNSS Experiment (MGEX) stations in China and its surrounding areas, the results show that, when separately using HAS and PPP-B2b products for PPP, the average accuracy in horizontal and vertical directions are (2.4, 2.7 cm) and (2.4, 2.0 cm), respectively. The average accuracy of C_H strategy is 2.1 and 1.7 cm, which was improved by 31.3% compared with separately using the products. Similarly, the average accuracy of C_B strategy is 2.1 and 1.9 cm, corresponding to improvements of 29.6%. When comparing the two combined strategies, it is noted that the C_B strategy converges faster. Based on the data from vehicle platform, the results show that the horizontal and vertical accuracy of the C_B strategy is 8.6 and 15.7 cm respectively. The accuracy improvement of C_B is better than that of C_H strategy, and the average accuracy is 68.4% better than that of separately using the products. The above results show that the two combined strategies can improve positioning accuracy. In addition, the improvements in accuracy and convergence speed of C_B strategy are more significant. Users are advised to use C_B strategy for the combination of HAS and PPP-B2b products, which will greatly expand the application of HAS and PPP-B2b services.

In recent decades, Global Navigation Satellite System-Interferometric Reflectometry (GNSS-IR) environmental parameters inversion has become a research hotspot in the field of GNSS. Among them, sea/water level inversion has become one of the applications with better inversion performance because of its clear mathematical relationship and horizontal reflection surface. Among the many sources of error in GNSS-IR sea level inversion, sea surface height variation is the most significant source of error. The key to correcting this error is the accurate estimation of the rate of change of sea surface height. However, the estimation of the rate of change is difficult to be accurate, making it difficult to correct this error precisely. Theoretically, the retrieval error results in an offset in the initial phase parameter in the signal-to-noise ratios (SNR) oscillation sequence. Therefore, the error can also be corrected by estimating the phase. However, the phase determined during parameter fitting is between − π and π. When the error affects the phase offset magnitude greater than 2π, the integer cycle of it is not available, resulting in the phase-based correction model not being able to correct the error. In other words, the integer cycle ambiguity that exists in GNSS positioning also exists in SNR phase determination. In this article, a method for integer cycle determination based on the assistance of the traditional sea surface height variation error model is proposed, and an error correction method based on SNR phase and a multi-mode multi-frequency combination inversion method are also proposed. Two GNSS sites with different tidal amplitudes are selected to carry out the experiments. The results show that the phase-based error correction method improves the sea-level retrieval accuracy by about twice as much as that obtained using the traditional correction method. Meanwhile, this paper analyses the adaptability of the phase-based error correction method: good results can be achieved in the lower elevation angle interval, while the results are poor in the higher elevation angle interval. This study provides another solution idea for GNSS-IR error correction based on phase parameters, and the accuracy improvement achieved by this method is significant.

The SMILE (Solar wind Magnetosphere Ionosphere Link Explorer) mission is a joint space science mission between the European Space Agency (ESA) and the Chinese Academy of Sciences (CAS), aiming to understand the interaction of the solar wind with the Earth’s magnetosphere in a global manner. The mission was adopted by CAS in November 2016 and by ESA in March 2019 with a target launch in 2025. The SMILE mission successfully passed the join mission Preliminary Design Review in 2020 and the joint spacecraft and mission Critical Design Review in June 2023. The SMILE spacecraft Flight Model is now in the final stage of Assembly, Integration and Test campaign which will be carried out at ESTEC in September 2024. It will then be shipped to the Kourou Space Centre in French Guiana for launch. This paper summarizes the SMILE mission development, design and status as of June 2024.

The least squares method is still commonly employed in traditional global navigation satellite system (GNSS) velocity estimation, but this method is easily biased by outliers from various sources. Random sample consensus (RANSAC) and solution separation (SS) algorithms have been employed in the domain of GNSS velocity estimation to identify and eliminate faults in the GNSS propagation process, yielding favorable outcomes. However, these algorithms are generally applied in single-epoch velocity estimation applications and use a single threshold for inspection and elimination, lacking adaptability to the observation environment. Therefore, a residual-based multithreshold constraint algorithm (RMCA) is proposed to improve the iterative results and obtain a time series solution of the GNSS velocity model. In the RMCA, the importance of residuals in the least squares approach is considered, and errors are directly expressed. Second, rather than employing a predetermined single threshold for exclusion, flexible threshold regulation is applied across various levels. Finally, the RMCA leverages the historically optimized velocity to establish sensible constraints on the current velocity estimation. Moreover, a mutual detection mechanism between GNSS velocity models is established. An experimental analysis of two groups of urban vehicles reveals that the velocity results obtained via the RMCA are more robust than those obtained via the traditional least squares algorithm and the SS scheme and are more continuous than those obtained via RANSAC. The RMCA is evidently well designed and efficient, demonstrating significant application value.

The Europa Imaging System (EIS) consists of a Narrow-Angle Camera (NAC) and a Wide-Angle Camera (WAC) that are designed to work together to address high-priority science objectives regarding Europa’s geology, composition, and the nature of its ice shell. EIS accommodates variable geometry and illumination during rapid, low-altitude flybys with both framing and pushbroom imaging capability using rapid-readout, 8-megapixel (4k × 2k) detectors. Color observations are acquired using pushbroom imaging with up to six broadband filters. The data processing units (DPUs) perform digital time delay integration (TDI) to enhance signal-to-noise ratios and use readout strategies to measure and correct spacecraft jitter. The NAC has a 2.3° × 1.2° field of view (FOV) with a 10-μrad instantaneous FOV (IFOV), thus achieving 0.5-m pixel scale over a swath that is 2 km wide and several km long from a range of 50 km. The NAC is mounted on a 2-axis gimbal, ±30° cross- and along-track, that enables independent targeting and near-global (≥90%) mapping of Europa at ≤100-m pixel scale (to date, only ∼15% of Europa has been imaged at ≤900 m/pixel), as well as stereo imaging from as close as 50-km altitude to generate digital terrain models (DTMs) with ≤4-m ground sample distance (GSD) and ≤0.5-m vertical precision. The NAC will also perform observations at long range to search for potential erupting plumes, achieving 10-km pixel scale at a distance of one million kilometers. The WAC has a 48° × 24° FOV with a 218-μrad IFOV, achieving 11-m pixel scale at the center of a 44-km-wide swath from a range of 50 km, and generating DTMs with 32-m GSD and ≤4-m vertical precision. The WAC is designed to acquire three-line pushbroom stereo and color swaths along flyby ground-tracks.