An efficient facet-based composite scattering model (FBCSM) is developed for calculating the time-evolving electromagnetic (EM) scattering field (TESF) to study the normalized radar cross section (NRCS) and Doppler spectrum characteristics from dynamic sea surfaces. The dynamic sea surface comprises two-scale profiles: small-scale capillary ripples modulated by large-scale gravity waves, which are modeled by millions of small facets. In microwave bands, two scattering mechanisms, quasi-specular scattering with respect to gravity waves and Bragg scattering with respect to ripples, are taken into account in the FBCSM for computation of the time-evolving EM scattering field under diverse polarizations. However, it may be very time-consuming and difficult to calculate the TESF due to the high resolution and dynamic complexity of the large dynamic sea surface. In this paper, the NVIDIA Tesla K80 GPU with the Compute Unified Device Architecture (CUDA) is utilized to improve the computational performance of the SPEF. The whole GPU-based TESF computation includes the optimal use of temporary variables, shared memory, constant memory and register, fast-math compiler options, asynchronous data transfer, and the most suitable block size and number of registers. By utilizing the proposed five optimization strategies, a significant speedup of can be achieved for computation of TESF from large dynamic sea surfaces for microwave bands compared with the single-threaded C program executed on the Intel(R) Core(TM) i5-3450 CPU.

The characteristics of sea clutter in real marine environments in different sea areas play a vital role in military industry, such as radar detection, remote sensing, SAR imaging, and situational awareness. In this article, a deep neural network (DNN) sea clutter model is proposed based on the sea clutter big data under the real marine environment to study the characteristics of sea clutter and parameter inversion. Based on the ERA-Interim reanalysis (2015–2017), a database of marine environmental elements in China's offshore waters was established, and a spatiotemporal prediction model for marine environmental elements was proposed to improve missing values. Considering the scattering mechanism of sea surface at different scales comprehensively, a large database of sea clutter time series of multiscale real surface is established and comparison with the experimental data. Aiming at the coastal waters of China, the long short-term memory model and DNN are used to establish the correlation model between marine environmental elements and sea clutter characteristics, and the prediction and parameter inversion of sea clutter characteristics based on sea clutter big data are studied. The results show that the coefficients of determination of the predicted fitted curves for the HH and VV polarization amplitudes reach 0.9249 and 0.8872, respectively. The inversion of wave heights in different sea areas is the lowest in the South China Sea (accuracy rate of 78%) and the highest in the East China Sea (accuracy rate is 90%). The results of this article can help improve the ability of sea surface remote sensing and sea clutter suppression.

An efficient parallel computation using graphics processing units (GPUs) is developed for studying the electromagnetic (EM) backscattering characteristics from a large three-dimensional sea surface. A slope-deterministic composite scattering model (SDCSM), which combines the quasi-specular scattering of Kirchhoff Approximation (KA) and Bragg scattering of the two-scale model (TSM), is utilized to calculate the normalized radar cross section (NRCS in dB) of the sea surface. However, with the improvement of the radar resolution, there will be millions of triangular facets on the large sea surface which make the computation of NRCS time-consuming and inefficient. In this paper, the feasibility of using NVIDIA Tesla K80 GPU with four compute unified device architecture (CUDA) optimization strategies to improve the calculation efficiency of EM backscattering from a large sea surface is verified. The whole GPU-accelerated SDCSM calculation takes full advantage of coalesced memory access, constant memory, fast math compiler options, and asynchronous data transfer. The impact of block size and the number of registers per thread is analyzed to further improve the computation speed. A significant speedup of 748.26x can be obtained utilizing a single GPU for the GPU-based SDCSM implemented compared with the CPU-based counterpart performing on the Intel(R) Core(TM) i5-3450.

The development of a time-evolving oceanic surface model (TOSM) is important for the accuracy of determining electromagnetic scattering properties from the sea surface in oceanic remote sensing, target detection, and synthetic aperture radar imagery at arbitrary incident angles, especially small grazing angles. The double superimposition model (DSM) is a well-known approach for TOSM, which is composed of large-scale gravity waves and small-scale capillary ripples superimposed on them. However, due to the real-time dynamic complexity and dimensionality of the TOSM, the traditional DSM algorithm may be very time consuming and hardly meet the real-time requirements. In this paper, the feasibility of using graphics processing units (GPUs) with diverse compute unified device architecture optimization techniques to increase the speed of the generation of the TOSM is investigated. The entirely GPU-based TOSM implemented in this study includes the most effective use of temporary arrays, fast-math compiler options, shared memory, more L1 cache use than shared memory, and page-locked host memory. With respect to single-threaded C programs running on Intel(R) Core(TM) i7-2600K CPU, optimization on the GPU-based generated TOSM can achieve a speedup of $791 \times $ by using a single NVIDIA Tesla K80 GPU model. This new GPU-based TOSM implemented shows great potential for studying the scattering characteristics of electromagnetic backscattered echoes from dynamic sea surfacess.

At present, 5G base stations are densely distributed in major cities based on improving user concentrations and the large demand for services in urban hotspot areas. Moreover, 5G communication requires more accurate communication propagation loss (PL) monitoring. Low-build areas, such as suburbs and rural areas, are prone to forming relatively stable tropospheric ducts, which can bend the signal to the surface in the duct-trapping layer for multiple reflections. Due to the random flow of the atmospheric air mass, each reflection of the communication signal is re-scattered in the troposphere through the top of the duct layer, thereby expanding the propagation range of the signal and changing the expected effect of radio wave propagation. If ducting and troposcatter effects happen in the 5G base station antenna layer, co-channel interference (CCI) could occur, affecting the quality of electromagnetic propagation. Urban links in the plain area have no major terrain obstacles, but ground fluctuations and land cover scattering have a greater impact on the signal scattering at the bottom of the duct. On the basis of the forward-propagation theory, this paper adds factors, such as ducting the forecast value using real weather parameters, terrain, and land cover-type distributions to evaluate the CCIs of over-the-horizon communications on the intercity link. Based on 1300 sets of randomly generated terrains and landforms, two deep learning (DL) models were used to predict the PL of over-the-horizon communications between cities in a land-based ducting environment. The accuracy of LSTM prediction could reach 98.4%. The verification of PL prediction using DL in this paper allows for quick and efficient prediction of PL in the land-based ducting of intercity links using land cover characteristics.

Based on marine environmental factors of different sea areas, a high-performance sea clutter time series modeling algorithm for the real sea surface is developed to study the amplitude mean and Doppler spectrum characteristics of sea clutter. The European Centre for Medium-Range Weather Forecasts (ECMWF) data set (ERA-Interim) and ESA’s soil moisture and ocean salinity (SMOS) data set are utilized to establish databases of different marine environmental factors. Combined with the mixed spectrum model, the geometric fine structure of wind-driven sea surface with swell superposition is established by using the double-superposition method (DSM) and comprehensively considering small-scale capillary ripples, large-scale gravity waves and swell. A triangle facet-based sea clutter series modeling algorithm is developed, in which the quasi-specular scattering based on a triangle and the scattering based on gravity wave modulation capillary spectrum are calculated, respectively, and compared with the measured results. For high-resolution radar, dynamic sea surface modeling and sea clutter calculation are very time consuming. In this paper, the Tesla K80 GPU manufactured by NVIDIA in Santa Clara, Computed Unified Device architecture (CUDA) high-performance parallel technology and some optimization strategies are adopted to improve the efficiency of sea clutter modeling. The results can be used to analyze the distribution characteristics of marine factors, the average amplitude and Doppler characteristics of sea clutter in different sea areas.