The diversity and complexity of the environment, such as wind speed, wave height, sea clutter, etc., are the main reasons for the current limitation in the spaceborne Global Navigation Satellite System-Reflectometry(GNSS-R) sea surface height(SSH) accuracy. This letter proposes a modified ResNet architecture for sea surface altimetry, named fractional Fourier transform-ResNet (F-ResNet), using filtered Delay-Doppler Maps(DDMs) and other information from Cyclone Global Navigation Satellite System(CYGNSS) Level 1 data. The DDMs are filtered by fractional Fourier transform(FrFT) to keep the doppler and delay information in the five maps according to the different fractions. F-ResNet is based on convolutional layers for the filtered DDMs, along with fully connected layers for processing ancillary technical and higher-level input parameters. The validity of our proposed method is demonstrated using the Jason-3 data set for the whole year 2020. The RMSE of the SSH using F-ResNet is improved by 15.2% compared to other machine learning methods, which can achieve 1.17 meters. And the influence of different signal-to-noise ratios on prediction accuracy is analyzed.

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.

Influenced by the randomnesses in the realistic vehicle-to-infrastructure (V2I) scenarios, traditional deterministic optimization algorithms cannot be adopted directly to promote sensing-communication performance. In this letter, we consider a vehicle served by several base stations (BSs), model the integrated performance based on information theory, and formulate an optimization problem to enlarge the total information amount. A reinforcement-learning-based algorithm of jointly optimizing the bandwidth allocation and BS selection is proposed, where a double deep Q-network agent is adopted. Simulation results verify the system performance, which shows performing better than baselines.

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.

To guarantee safe and stable operation of the offshore oil rigs, it is important to monitor the status of the oil rigs and the marine hydrological environment around the oil rig. This work first demonstrates and evaluates the potential use of global navigation satellite system (GNSS) technologies in the oil rigs. GNSS real-time kinematic (RTK) is used to monitor the quasi-static displacements and the dynamic responses of the oil rigs. In an 11-month experiment, GNSS-RTK successfully measures the quasi-static displacements with horizontal and vertical changes of $\sim 10$ and $\sim 15$ mm, respectively, when the temperature changes from $-10^{\circ} \text{C}$ to $30~^{\circ} \text{C}$ . This suggests that GNSS-RTK can be used to monitor tiny quasi-static displacements of the oil rigs. For the dynamic response, the experimental results show that the vibration of the oil rigs has no correlation with significant wave height (SWH) and wind speed, respectively, below 1.0 m and 8.0 m/s, and the dominant frequency and vibration amplitude are, respectively, around $\sim 0.35$ Hz and $\sim 5$ mm. However, when SWH and wind speed, respectively, are over 1.0 m and 8.0 m/s, the dominant frequency and vibration amplitude of the oil rigs show strong correlations with SWH and wind speed and, respectively, decreases and increases with SWH and wind speed increase. These variations agree with the physical truths so that it is potential to use GNSS-RTK to monitor the dynamic responses. To provide the SWH and wind speed around the oil rigs, a real-time GNSS-reflectometry (GNSS-R) system is developed and experimentally tested. In a 24-day experiment, the retrieved SWH and wind speed are in agreement with the in situ data with root mean square errors (RMSEs) of 0.37 m and 1.40 m/s, respectively. Finally, an integrated and monitoring scheme of GNSS-RTK and reflectometry for the oil rigs is discussed.

The integrated sensing and communication-enhanced (ISAC-enhanced) intelligent transportation system (ITS) has great development potential and market value in future urban vehicle-to-infrastructure (V2I) scenarios. With limited total power consumption, joint design of power distribution and beam steering of sensing and communication subsystems is required to achieve optimal system performance. This paper introduces an intelligent power allocation and beam-steering control system, where both the system actions are determined based on the radar sensing data cube. The adaptive power allocation module employs the Actor-Critic reinforcement learning agents to optimize power distribution based on vehicle GPS data, communication channel capacity, and historical power allocation, balancing the overall system performance including the sensing and communication functions. The automatic beam control module, utilizing the convolutional neural network (CNN) for dynamic beam control, intelligently adapts communication beams for vehicles navigating complex roadways, enhancing the beam tracking accuracy and quality of service. Validation on real-world datasets showcases the effectiveness of these modules, highlighting the potential to enhance vehicular communication performance.