This paper presents a monitoring system based on Global Navigation Satellite System (GNSS) reflected signals to provide real-time observations of sea conditions. Instead of a computer, the system uses a custom-built hardware platform that incorporates Radio Frequency (RF), Field Programmable Gate Array (FPGA), Digital Signal Processing (DSP), and Raspberry Pi for real-time signal processing. The suggested structure completes the navigation signal’s positioning as well as the reflected signal’s feature extraction. Field tests are conducted to confirm the effectiveness of the system and the retrieval algorithm described in this research. The entire system collects and analyzes signals at a coastal site in the field experiment, producing sea surface wind speed and significant wave height (SWH) that are compared to local weather station data, demonstrating the system’s practicality. The system can allow the centralized monitoring of many sites, as well as field experiments and real-time early warning at sea.

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 elevation angle influence on coastal GNSS-R ocean code-based altimetry for GPS signals (L1 C/A and L5) and BDS B1 signals is investigated, and the corresponding correction method is presented. The study first focuses on the coastal ocean altimetry method, including the general experiment geometry and the code delay estimation using the single-point tracking algorithm. The peak power and the maximum first derivative are used as the location of the specular point. Then, the sensitivity of the height retrieved using the above coastal ocean altimetry method to elevation angle is analyzed based on the Z-V model. It can be seen that the elevation angle has a significant influence on the height retrieval, which will affect the precision of the coastal GNSS-R ocean altimetry. Finally, two correction methods, the model-driven method and the data-driven method, are proposed. The coastal altimetry experiments demonstrate that the correction methods can correct the elevation angle influence, and the data-driven method is more effective. The experimental results show that, after correcting the elevation angle influence, the code-based altimetry precision of the GPS L1 C/A signal, L5 signal, and BDS B1 signal can be up to the meter level, decimeter level (less than 4 decimeters), and meter level with respect to a reference tide gauge (TG) data set, respectively, without smoothing over time. These results provide information to guide the sea surface height retrieval using coastal GNSS-R, especially multi-satellite observation and GNSS signal with a higher chipping rate.

Due to the diffuse scattering on sea surface and dynamic geometry of coastal GNSS- R, the altimetry performance is strong dependent on elevation angle. As elevation angle decreases, the bias in retrieved height using DER and HALF retracker increases. In this paper, an multi-parameter estimator is proposed to combine the DER retracker and several HALF retrackers to obtain unbiased height. The simulated results show that the bias of the uncalibrated height using proposed algorithm has no significant dependence on elevation angle, but the RMSE for the low elevation angle case still are larger than that of the high elevation angle. In addition, the calibrated performance depends on the used fractional values so that proper fractional values should be chosen. The data from Dongying experiment acquiring GPS L1 CA and L5 signal and Qingdao experiment for BeiDou B1I signal are used to demonstrate proposed algorithm. After calibrating using proposed algorithm, regardless of GPS L1 CA, L5 or BeiDou B1I signal, the calibrated heights all are agreement with in-situ heights. Finally, the calibrated heights from BeiDou B1I signal are used to retrieve sea surface height. The results show that retrieved sea surface heights from GEO and IGSO/MEO satellites all appear the same trend with in-situ ones, furthermore, GEO satellites can provide the better retrieved performance than IGSO/MEO satellites. Through averaging the heights from several satellites and smoothing with the half-hour span, compared to in-situ sea surface height the RMSE of 0.67 m is obtained.

This paper presents the statistical analysis of red 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 presences of the coherent and non-coherent components, the phase of the reflected signal is not uniformly distributed with [−π, π]. The autocorrelation functions 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 RMSEs of 1.66 m/s and 1.75 m/s. When a minimum variance estimator is used to fuse two retrieved wind speeds, the RMSE reduced to 1.46 m/s. The work is helpful for developing a GNSS signal scattering model over sea surface, and the further studies of coastal GNSS-R to monitor sea state and maritime target.