The Global Navigation Satellite System (GNSS) provides continuous high-precision positioning, enabling many applications such as vehicle navigation and pedestrian monitoring. However, in challenging environments such as urban canyons, positioning accuracy is significantly degraded due to multi-path and non-line-of-sight (NLOS) issues. To tackle this issue, we introduce a single point positioning (SPP) framework based on pseudorange correction, including three modules: feature extraction, pseudorange correction, and positioning model. Heavy pseudorange error is the primary cause of inaccurate localization, and in particular, we introduce GNSSFormer, a transformer-based model to obtain the pseudorange correction values. After preprocessing raw observations of GNSS, we designed a pseudorange correction model GNSSFormer containing a temporal transformer block and a multi-satellite joint spatial transformer block. GNSSFormer extracts multiple features related to both satellites and receivers, learning the complex global relationships between these features and the pseudorange errors to derive correction values. Subsequently, an extended Kalman filter (EKF)-based rauch tung striebel (RTS) smoothing SPP algorithm is employed to determine the location. Validation demonstrates that GNSSFormer significantly enhances SPP performance compared to state-of-the-art algorithms, improving positioning accuracy at least by 29.42% and 28.40% on two open source datasets and 18.91% on real-world dataset.

The utilisation of global navigation satellite system reflectometry (GNSS-R) signals in remote sensing of land surface parameters has undergone significant advancements over the years. However, a paucity of analysis exists regarding the vegetation-transmitted GNSS signal, which represents an avenue for further research. In this study, an empirical investigation was conducted to ascertain the statistical characteristics of the power associated with GNSS signals emitted from vegetation, and the most appropriate distribution function model was obtained by a combinatorial test. The experimental results indicate that the vegetation-transmitted GNSS signal continues to conform to the characteristics of a Normal (right-hand circular polarization, RHCP) and Weibull (left-hand circular polarization, LHCP) distribution; however, significant variations are observed in the distribution parameters and the parameter value ranges. Furthermore, the results suggest a positive correlation between the k-order (k= 1, 2, 3, 4) moment order and the discrepancy in signals obtained by disparate GNSS antennas. Both antenna elevation angle and vegetation type exert an influence on moments of all orders, and the influence of the latter is more pronounced, thereby enabling the differentiation of vegetation types.

Global Navigation Satellite System-Reflectometry (GNSS-R) provides an effective remote sensing technique for accurate retrieval of sea surface height (SSH) measurements. However, accuracy is severely affected by environmental disturbances such as wind-induced sea clutter and wave interference, degrading Delay-Doppler Map (DDM)-derived measurements. In this study, we propose an advanced trajectory-based deep learning model, Crossformer, explicitly designed to capture temporal dependencies inherent in GNSS-R sequential data. The method leverages five distinct DDM features: Peak Power Point (PPP), Maximum Slope Point (MSP), Center Pixel Intensity (CPI), Average Power Point (APP), and Kurtosis (KUR). A dimension-segment-wise embedding technique combined with a two-stage attention mechanism effectively models both temporal and cross-dimensional correlations. Evaluation using CYGNSS data validated against Jason-3 Level 2 measurements demonstrates the superior performance of our approach, yielding a Root Mean Square Error (RMSE) of 0.93 m, Mean Absolute Error (MAE) of 0.65 m, and a coefficient of determination (R2) of 0.9901. Comparative analyses with baseline methods confirm significant improvements in robustness and predictive accuracy, particularly across varying sea states. This research underscores the potential of advanced temporal modeling techniques in GNSS-R altimetry applications.

A novel basic observable, termed the alternating-current texture (ACT), is defined from the original linear-polarization carrier-to-noise ratio (CNR) of Global Navigation Satellite System (GNSS) for monitoring wind vector. The amplitude distribution (AD), autocorrelation function (ACF), power spectral density (PSD), and fractal dimension (FD) of the ACT are explored. The results show that these statistical characteristics derived from the GNSS-IR CNR resemble those directly extracted from the reflected signal. Twelve statistics from the AD, ACF, PSD, and FD, as observables sensitive to wind speed, are analyzed. The findings suggest that these statistics exhibit geometric dependence, especially at the low elevation angle. Furthermore, the skewness of the AD, the correlation time of the ACF, PSD peak, PSD width, and fractal dimension respond more effectively to wind speed than other statistics, and are used to assess the capability of retrieving wind speed. Kernel principal component analysis (KPCA) is employed to fuse these statistics to produce a new sensitive observable to wind speed. When elevation and azimuth angles are confined to optimal regions with minimal interference, a root mean square error (RMSE) of 1.57 m/s is obtained with a minute-level temporal resolution. In contrast, a geodetic GNSS receiver provides an RMSE of 2.55 m/s. Additionally, based on the anisotropy of sea surface, the feasibility of these statistics in retrieving wind direction is investigated. Novel sensitive observables to wind direction, derived from the best-fit ellipse to the spatial distributions of the statistics, are defined. The fusion using KPCA achieves the best determination coefficient (DC) of 0.50 with wind direction, while the geodetic receiver yields a DC of only 0.16. These results conclude that low-cost GNSS sensors can be utilized for retrieving wind vector, and the linear-polarization GNSS-Interferometric Reflectometry (GNSS-IR) outperforms its right-handed circular polarization (RHCP) counterpart.

This paper revisits the Interferometric Complex Field (ICF) concept in coastal Global Navigation Satellite System-Reflectometry (GNSS-R), and proposes a novel baseband signal processing scheme for monitoring sea states. The scheme utilizes incoherent averaging to reduce hardware complexity while maintaining accuracy comparable to correlation-based methods. This eliminates requirements for the RHCP antenna, RF front-end, and baseband processor of the direct signal. Compared to the existing schemes, it has lower computational complexity, power, and cost. The primary observable, termed the alternating-current incoherent average power (aIAP), is derived by detrending a stable baseline component from the output of the proposed scheme. The coherence time and spectral width from the aIAP time series are defined as the observables of retrieving sea state, specifically wind speed and significant wave height (SWH). Three experimental data sets are used to demonstrate and assess the proposed scheme. Results indicate that the coherence time and spectral width of aIAP exhibit sea-state dependencies similar to those of alternating ICF within a wind speed range of 0 ∼ 15 m/s, and thus can be used to retrieve sea state. Spectral width retrieves sea state more effectively than coherence time. Coherence time and spectral width weakly depend on elevation angle so that an elevation correlation is unneeded. The proposed scheme, as a cost-efficient alternative, has the potential for operational sea state monitoring.

The potential of spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) is explored for detecting and estimating global mesoscale oceanic eddies. A fractional Fourier transform-residual network (F-ResNet) model is used to achieve centimeter-level absolute dynamic topography (ADT), demonstrating a root mean square error (RMSE) of 5.05 cm, which is subsequently used for detecting oceanic eddies based on their height characteristics. Using CYGNSS data from 2020, comprising over 750 million samples, the research demonstrates that GNSS-R technology effectively supports daily oceanic eddy detection, achieving precision rates of 50.63% and 47.80%, recall rates of 89.95% and 90.48%,$F1$scores of 64.79% and 62.56%, and accuracy rates of 47.92% and 45.52% for cyclonic and anticyclonic eddies, respectively. Although the method proves both effective and feasible, its performance remains suboptimal. To improve detection accuracy, the study explores high-bandwidth signals, whose sharper autocorrelation function enhances ADT retrieval and improves oceanic eddy detection accuracy. Simulation results from the ACE-BOC (15, 10) signal suggest precision rates of 85.51% and 84.14%, recall rates of 91.93% and 91.11%,$F1$scores of 88.76% and 87.48%, and accuracy rates 79.80% and 77.75% for cyclonic and anticyclonic eddies, respectively. Moreover, supplementary metrics for eddy features indicate significant improvements with high-bandwidth signals. The application of these two signals for sustained, long-term monitoring of oceanic eddies in a specific region is also demonstrated, providing a foundation for eddy tracking. The tracking results show that the ACE-BOC (15, 10) signal can effectively track eddy trajectories, achieving an RMSE of 1.11 km, whereas CYGNSS yields an RMSE of 8.4 km. Finally, this article discusses the characteristics of ACE-BOC and carrier-altimeter signals, highlighting the potential of advanced spaceborne GNSS-R altimeters and discussing future directions in this field.

This study provides statistical characteristics of the power of the reflected GNSS signals and establishes associations between probability density function (PDF) characteristics and different land cover situations. The feasibility of utilizing the PDF of signal power for determining the presence of vegetation cover and inverting associated parameters has been demonstrated. The simulation demonstrates that the signal-to-noise ratio (SNR) of land surface-reflected signals from various reflectors follows a Weibull distribution for the dual-antenna model, and an F distribution for the single-antenna model. The moment-generating function is used for the calculation of 1st-4thorder moments to study the characteristics of the PDF. Furthermore, the distribution parameters and moments are influenced by both the land-cover type and the reflector’s physical parameters. Experiments were conducted on mud flats and farmland for four months to validate the simulation model utilized in this study. Moreover, the simulation and experimental results demonstrate a mathematical correlation between the moments of the PDF and land surface parameters such as soil moisture content (SMC), soil roughness, and vegetation density.

This article presents the statistical analysis of the reflected global navigation satellite system-reflectometry (GNSS-R) signal from a coastal experiment, including the non-Gaussianity, probability distribution functions, autocorrelations, and fractal dimensions of the speckle and texture components. The results clearly show that the amplitude distribution is modeled well by a Weibull model. The texture component of the reflected GNSS signal has a log-normal distribution. Due to the presence of the coherent and non-coherent components, the phase of the reflected signal is not uniformly distributed with$\left [{{-\pi, \pi }}\right]$. The autocorrelation functions (ACFs) of the speckle and texture components both are Gaussian-shaped, with the correlation times on the order of hundreds of milliseconds and a few seconds, respectively. Some statistical properties of the reflected GNSS signal depend on GNSS-R geometry and sea state; therefore, once the influence of GNSS-R geometry is corrected, they can be used to determine sea state. The speckle and texture correlation times of the reflected GNSS signal, as an example, are used to retrieve wind speed. The speckle and texture correlation times provide retrieved wind speeds with root mean square errors (RMSEs) of 1.66 and 1.75 m/s. When a minimum variance estimator is used to fuse two retrieved wind speeds, the RMSE is reduced to 1.46 m/s. The work is helpful for developing a GNSS signal scattering model over the sea surface and further studies on coastal GNSS-R to monitor sea state and maritime target.

Spaceborne global navigation satellite system reflectometry has been used to retrieve wind speed, but few studies have directly detected tropical cyclones from spaceborne GNSS-R observations. Moreover, it is challenging to solve the multivalued dependence of the spaceborne observable on wind speed to unambiguously and accurately retrieve wind speed in cyclone conditions. This paper presents a single-pass cyclone detector and coarse estimation approach of the tropical cyclone position using a spaceborne GNSS-R full delay-Doppler map (FDDM). When the specular point passes through a tropical cyclone, the FDDM asymmetry experiences an abnormal change. From the FDDM asymmetry sequence along the specular point trajectory, two subsequence features, including the slope and extremum difference, are defined, from which the tropical cyclone detector is proposed. The results from the simulation and the Cyclone GNSS (CYGNSS) data both indicate a good detection performance for tropical cyclones. The location corresponding to the peak detector is considered as a coarse estimation of the tropical cyclone position. The test result from the CYGNSS data shows that the mean error between the detected cyclone center and the International Best Track Archive for Climate Stewardship (IBTrACS) cyclone center is approximately 124.50 km. From the detector of tropical cyclones, a scene-classified wind speed retrieval model is proposed. The simulated and experimental results show that a better retrieval performance can be obtained at high wind speed (> 30 m/s) using the scene-classified model. Reductions of 10 m/s and 4 m/s in the root mean square errors (RMSEs) are obtained for the simulation and CYGNSS data, respectively. This work is meaningful for directly detecting tropical cyclones and retrieving high wind speed data using spaceborne GNSS-R in real time.

The signals at higher elevation angles could be received by a left-handed circularly polarized (LHCP) antenna because LHCP components dominate the reflected signals at higher elevation angles. This letter explores a four-channel interference method of dual-antenna global navigation satellite system (GNSS) reflectometry capable and its application in retrieving the water level. Compared to single-antenna observation using a oscillating signal-to-noise ratio (SNR), due to the capability of receiving the reflected signals at high elevation angles, the proposed method has higher temporal resolution. The criterion based on the effective value of the time series is proposed to evaluate the quality of oscillating carrier phase difference. The Lomb–Scargle method and cosine fitting are used to estimate the oscillated frequency of carrier phase difference. Through normalizing cosine oscillation and time scale by the GNSS signal amplitude and wavelength, the time-series from multisatellites could be combined to retrieve the height. At last, the experiment is conducted to demonstrate the proposed method, and the results show that when the criterion threshold is under 0.2, the centimeter-level precision of the retrieved height could be achieved. Two-satellite observation combining satellite pseudorandom noise (PRN) 15 and 24 could decrease the time span of the observation to 320 s from 480 s for only using single satellite, and the root mean square error (RMSE) reduces to 6.2 cm from 9.5 and 18.9 cm of the satellite PRN 15 and 24.

For spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R), the delay difference of direct and reflected GNSS signals among successive snapshots changes rapidly because of the high dynamics of low earth orbital and GNSS satellites. This change has to be compensated to avoid the distortion of incoherently averaged delay-Doppler map (DDM). The method to refresh the correlation window on each coherent integration time period may require too many instrument resources or too much data to be uploaded from the ground station. This letter proposes a new postprocessing approach based on the motion degradation model of DDM and the reconstruction to replace real-time compensation. Raw sampled data from UK TechDemoSat-1 are used to verify the availability of proposed approach. The results show that after reconstruction for the distorted DDM, the DDM accuracies relative to that compensated in real time are significantly improved.

For preventing the effects of sea ice bring the serious negative impact on the maritime transport, a full-scale detection of sea ice should be performed. The BeiDou GEO satellites could provide stable geometry and better coverage in mid- and low-latitude region where most of the sea ice occur. Based on this consideration, this paper evaluates the usage of BeiDou GEO Satellites reflected signals for accurate real-time Earth observation to study the changes in the sea surface state through remote sensing. BeiDou GEO signals received after reflection from Bohai Bay were analyzed for their sea ice content. The results are in good agreement with the cluster center of the sea ice reflected correlation power and the sea water reflected correlation power. The average of the cluster center on the sea ice surface is much smaller than that on the sea water surface.

This letter focuses on exploiting parameters, including peak power spectral density (PSD), integrated power, mean frequency, and spectrum width of reflected BeiDou signals in the frequency domain, to retrieve wind speed. The PSD of reflected signals is estimated after choosing proper parameters of the Welch method. Then, the features of PSD are illustrated, and a method is proposed based on fitting estimated PSD with a Gaussian function to evaluate the aforementioned PSD parameters. The estimated parameters of collected BeiDou medium earth orbit (MEO)/inclined geosynchronous satellite orbit (IGSO) and geostationary orbit (GEO) data are fitted with in situ wind speed using polynomial. Fitting results show that the peak PSD, mean frequency, and spectrum width of reflected signals from GEO have more evident dependence on wind speed, compared with MEO/IGSO. To obtain the most accurate results, the impact of delay used in lagging direct replica to align reflected signals is analyzed. Results show that regardless of delay locating within the interval of [τ0- τc, τ0+ τc], the better root-mean-square error (rmse) of less than 1.7 m/s and the larger coefficient of determination over 0.8 can be obtained by retrieving from the spectrum width, compared with peak PSD and mean frequency, whose optimal results, with rmse values of 2.03 and 1.70 m/s and coefficient of determination values of 0.73 and 0.81, are obtained as delay is τ0- 0.85τcand τ0+ 0.9τc, respectively, where τ0is the delay of specular reflection, and τcis the length of the B1 code.