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.

This paper proposes a hyperspectral imaging simulation method based on a ray-tracing algorithm. The algorithm combines calculations based on solar and atmospheric visible light radiation as well as the spectral bidirectional reflection distribution function (BRDF) of the target surface material and can create its own scenarios for simulation calculations on demand. Considering the presence of multiple scattering between the target and background, using the ray-tracing algorithm enables the precise computation of results involving multiple scattering. To validate the accuracy of the algorithm, we compared the simulated results with the theoretical values of the visible light scattering intensity from a Lambertian sphere. The relative error obtained was 0.8%. Subsequently, a complex scene of engineering vehicles and grass was established. The results of different observation angles and different coating materials were calculated and analyzed. In summary, the algorithm presented in this paper has the following advantages. Firstly, it is applicable to geometric models composed of any triangular mesh elements and accurately computes the effects of multiple scattering. Secondly, the algorithm combines the spectral BRDF information of materials and improves the efficiency of multiple scattering calculations using nonuniform sampling. The computed hyperspectral scattering data can be applied to simulate airborne or space-borne remote sensing data.

Inverse synthetic aperture LiDAR (ISAL) breaks through the limitations of the diffraction limit and achieves ultra-long-distance radar imaging with centimeter-level resolution. However, because ISAL obtains a high resolution, it is accompanied by a high sampling rate and a large data volume, and the processing process is complicated, which is not conducive to fast real-time imaging of ISAL targets. At the same time, considering that actual non-cooperative targets cannot obtain full-angle ISAL images during movement, in this paper, the Kirchhoff approximation method based on two-dimensional Fourier transform is used to calculate the scattering echo of the rough surface of the target, and then, the rough surface scattering echo of the target coordinate system is obtained through coordinate transformation. After vector superposition, the scattered echo and ISAL image of the rough target are finally obtained, and then the influence of the rotation angle on the ISAL imaging of the rough plate and the rough target is discussed. It is found that a small rotation angle range can also achieve clear ISAL imaging of rough targets, and the influence of different roughness on the ISAL imaging results of different rough targets under a small rotation angle is analyzed. When the roughness is decreased, the target scattering mainly comes from coherent scattering, and the target edge becomes sharper. As the roughness increases, the image energy distribution becomes more uniform. Theoretical and simulation experiments verify the feasibility of ISAL imaging of rough targets under small rotation angles.