In this paper, we demonstrate a real-time, multi-user downlink underwater wireless optical communication (UWOC) system for practical applications via field programmable gate arrays (FPGAs). The performance of the established UWOC system is experimentally investigated under diverse channel conditions. The established UWOC system utilizes arrayed light emitting diodes (LEDs) as the transmitter and employs optical superimposition-based non-orthogonal multiple access (NOMA). The feasibility of employing the arrayed LEDs as the transmitter under different channel conditions is validated by simulating the optical intensity distributions of LED groups. Both the simulation and experimental results reveal that the bit error rate (BER) of user 1 (higher power user) decreases with increasing power allocation ratio (PAR), while the BER of user 2 (lower power user) increases with the increase in PAR. Uniform post-equalization is employed to extend the bandwidth of each LED group. Besides, a metal-oxide-semiconductor field-effect transistor (MOSFET)-based driver circuit and a remaining carrier sweep-out circuit unit (CSCU) are proposed to facilitate the independent control over different LED groups and enhance the response speed of LEDs, which outperform bias-tee and the single MOSFET-based driver circuit. Experimental results also indicate that NOMA exhibits superior spectral efficiency compared to the conventional time division multiple access (TDMA). A PAR of approximately 2:1 is appropriate to ensure both users operate within the forward error correction (FEC) threshold. Based on the proposed schemes, the real-time UWOC system achieves a data rate of 40 Mbps for both users with BER below FEC limit under different channel conditions. The results highlight the significant potential of the designed UWOC system to effectively meet diverse real-time, multi-user UWOC application requirements.

The global sea surface salinity (SSS) can be accomplished by means of the L-band microwave radiometers from space, such as European Space Agencys SMOS (Soil Moisture and Ocean Salinity) and NASAs Aquarius/SAC-D. However, the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) in SMOS mission suffers from various errors and unexpected in-orbit phenomena, which degrade SSS accuracy. To further improve SSS accuracy, a Full Polarization Aperture Synthesis Microwave Radiometer (FPASMR) has been proposed for Chinas next generation Haiyang (HY) satellite program with two important improvements, which is composed by an L-band and X-band aperture synthesis radiometer (ASR) with two dimensions. One important improvement is that the sea surface roughness (SSR) and sea surface temperature (SST) are simultaneously measured by the full polarization X-band ASR, and another important improvement is that the microwave-optical receiving technology and in-orbit thermal control subsystem are employed to improve the stability of the FPASMR for reducing visibility phase errors. The system of the FPASMR is introduced in terms of L-band subsystem, X-band subsystem and the calibration subsystem. The main specifications of the FPASMR are analyzed in terms of field of view (FOV), angular resolution and radiometric sensitivity. In addition, the FPASMR calibration is also introduced. The FPASMR demonstrator has also been developed and a series of experiments is performed to assess the performance of the FPASMR demonstrator and the improvements in the FPASMR.

Sea surface salinity (SSS) is a fundamental parameter for understanding ocean phenomena and plays a vital role in studying global climate change and weather prediction models. Following the earlier launch of Soil Moisture and Ocean Salinity (SMOS), Aquarius, and Soil Moisture Active Passive (SMAP) satellites, the Chinese Ocean Salinity and Soil Moisture Mission (COSM) was successfully launched on November 14, 2024. The launched satellite is equipped with the 2-D L-band Aperture Synthesis Microwave Radiometer (LASMR) and the Microwave Imager Combined Active and Passive (MICAP) components to gather high-precision SSS information. This paper presents the Airborne LASMR (ALASMR), which features a Y-shaped two-dimensional synthetic aperture microwave radiometer and contains 11 antenna units with a unit spacing of 0.82λ. The ground test investigations of the ALASMR have been conducted to evaluate antenna patterns, test the sensitivity of receiving channels, and conduct ocean aviation experiments. To examine the flight observation, uniform salinity is assumed for the sea area obtained from the calibration platform of National Satellite Ocean Application Service (NSOAS) whereas the salinity gradient is taken for the Laizhou Bay area. The ALASMR exhibits enhanced imaging performance, with a spatial resolution of about 0.35 km and a width of about 1.24 km at the flight altitude of 1.2 km. The retrieval of SSS in the sea area of the ocean calibration platform is also demonstrated in this work. This indicates that ALASMR can reliably execute salinity observation of near-shore with high precision and provide a cross-calibration data source for COSM after its launch.

The effective calibration of systematic errors is a precondition for high-quality images from a synthetic aperture radiometer system. The receiving channel errors can be relatively well calibrated, but antenna errors cannot be calibrated effectively in-orbit. This paper proposes an in-orbit external antenna error calibration method for synthetic aperture radiometers using external calibration point sources on the ground. Simulation and experimental results demonstrate the accuracy and feasibility of the method. The layout of on-ground calibration point sources was then explored. Our results indicate that is sufficient to adjust the power of the calibration point sources when SNR is 30 dB, and that the calibration requirements can initially be met with three calibration point sources. If better calibration effect is required, eight calibration point sources are sufficient. Non-repeated and uniform azimuth angle schemes appear to be the optimal layout for operating the proposed method, but adjustments can be made as necessary to suit the situation at hand. Finally, a feasible implementation scheme was established. The proposed method has certain reference value in terms of image quality improvement and practical engineering applications.

This paper proposes a method for calculating the millimeter-wave (MMW) radiation of chaff corridor-target-sea scene. This method is used to analyze the attenuation of target brightness temperature (TB) by chaff corridor. Firstly, a numerical method is proposed to obtain the facet model of chaff corridor, which is then combined with the facet models of sea and target obtained by traditional method. Secondly, ray tracing method is carried out to obtain the MMW radiation TB of the scene. In the ray tracing, the emissivity, reflectivity, and transmittance of chaff corridor facets are calculated by generalized equivalent conductor (GEC) method and the calculation method of reflection and transmission by a layered medium (RTLM). The emissivity and reflectivity of sea and target facets are calculated by their permittivity. Lastly, to verify the correctness of the MMW radiation calculation method, a method for measuring the emissivity, reflectivity, and transmittance of chaff corridor is proposed. The TB of chaff corridor-metal plate and chaff corridor-ship-sea scenes are simulated and measured to analyze the TB attenuation caused by chaff corridor. The results show that the calculation method is effective and the attenuation by chaff corridor decreases with the increase of TB.

Aperture Synthesis Radiometer (ASR) is an important instrument used in Earth remote sensing observations and has significant implications in areas such as climate change research and environmental monitoring. Nevertheless, conventional ASR calibration techniques exhibit inherent limitations, such as direction diagram correction and internal noise injection. This paper introduces ASRC-Net, a deep learning-based calibration method for ASR that can improve calibration accuracy. Firstly, the method utilizes a deep learning network (ASRC-Net-1) to conduct feature learning and extraction on spectrum data gathered by ASR, facilitating brightness temperature (BT) reconstruction and relative calibration. Subsequently, by integrating reference BT scene data, such as flat sea surfaces and employing appropriate network structure, loss functions and optimization algorithms, an absolute calibration network for ASR (ASRC-Net-2 network) is established. The network undergoes rigorous training and validation utilizing a substantial volume of measured data. Experimental findings attest that the proposed deep learning-based ASR calibration method notably enhances calibration accuracy. This method heralds a novel approach and technical avenue for precise ASR measurement, holding substantial application value and promising prospects for widespread adoption. Furthermore, it furnishes technical underpinning for high-precision in-orbit measurement of Chinese ocean salinity detection satellites.