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The parabolic equation (PE) model is effective for predicting signal propagation over irregular terrains. The shift map method of the PE model is highly accurate and widely used for terrain propagation predictions. The maximum propagation angle is a crucial parameter of the shift map model. Here, unreasonable values may result in incorrect results or further computations. However, the prediction formula used in existing literature is limited, thereby leading to inaccurate results. In this study, a maximum propagation angle prediction method was developed. The maximum propagation angle with terrain should be calculated as the sum of the maximum propagation angle without terrain and twice the maximum terrain slope angle. Hence, the selection criteria for the different models in the hybrid terrain model were reanalyzed. An accurate maximum propagation angle is key for the PE model over irregular terrains, as it ensures prediction accuracy and improves calculation speed. [ABSTRACT FROM AUTHOR]

Millimeter-wave channel modeling is the basis of fifth-generation (5G) communication network design and applications. In urban microcellular environments, the roughness of wall surfaces can be comparable to the wavelengths of millimeter waves, resulting in walls that cannot be considered as smooth surfaces. Therefore, channel modeling methods based on only traditional three-dimensional ray tracing (RT) or the three-dimensional parabolic equation (PE) result in the limited computational accuracy of millimeter-wave channel models for urban environments. Based on the scattering theory of a rough surface and the typical scattering characteristics of a millimeter wave, the end field of the three-dimensional vector PE is regarded as the initial field of three-dimensional RT. Moreover, the number of scattered rays and scattering angles are introduced. Finally, a three-dimensional fully polarized millimeter-wave hybrid propagation channel model (3DFPHPCM) is proposed. The proposed model improves the computational accuracy of a single deterministic model. Millimeter-wave channel measurements in non-line-of-sight (NLOS) environments were carried out to verify and optimize the proposed 3DFPHPCM. The results show that the root mean square error (RMSE) and mean absolute error (MAE) of the proposed 3DFPHPCM are both minimized when compared to three-dimensional RT or the three-dimensional PE, which indicates that the proposed 3DFPHPCM has higher computational accuracy. Moreover, its runtime is the shortest among the methods. The results presented herein provide technical support for the layout of base stations. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Complex-terrain clutter presents serious nonuniformity, which has a significant impact on radar target detection, communication, and navigation. The accurate acquisition of the clutter characteristics by measurement or calculation for large and complex terrains has always posed a challenge due to the high costs of the measurement, as well as the intricate and diverse environmental factors. To address this challenge, we proposed a research methodology that leverages the similarity of multidimensional terrain features to infer the clutter characteristics of unmeasured regions, particularly those that are difficult or impossible to measure directly. In order to realize this study object, we constructed a dataset consisting of multidimensional environmental and clutter data to quantitatively characterize the complex environmental information in a vast territory. Within the dataset, we selected two regions with similar terrain characteristics: one region served as the source data for mining and analyzing features, while the other was designated as the target data region for method validation. Through the application of prior-knowledge-based classification and multifactor weight analysis on the dataset, two novel estimation techniques were devised. The first method, designated as PCKRF, blended prior-knowledge classification, weighted K-means clustering, and the random forest (RF) algorithm; and the second method, labeled PCKMW, integrated prior-knowledge classification, weighted K-means clustering, and the minimum weighted distance (MW) approach. In estimating and validating the clutter data from the source region to the target region, the performances of both the PCKRF and PCKMW methods were notably superior to those of the RF, MW, and K-means minimum weighting (KMW). Specifically, the root-mean-squared error (RMSE) was enhanced from a range of 7 dB–10 dB to a range of 4 dB–6 dB, while the determination coefficient (R2) was increased from a range of −1.15–0.09 to a range of 0.25–0.66. The above demonstration illustrates that the current achievements in the clutter estimation methods offer a viable option for accurately recognizing clutter characteristics in complex-terrain environments where comprehensive data collection may be difficult or impossible, with lower human and economic costs. [ABSTRACT FROM AUTHOR]

The terahertz scattering characteristics of metallic and dielectric rough targets is important for the investigation of the terahertz radar targets properties. According to the stationary phase theory and scalar approximation, if the radius of curvature at any point of the surface is much larger than the incident wavelength, and the wavelength is also much longer than the surface height function and Root-Mean-Square (RMS) surface slope, the coherent and incoherent scattering Radar Cross Section (RCS) of rough metallic and dielectric targets can be obtained. Based on the stationary phase approximation, the coherent RCS of rough conductors, smooth dielectric targets and rough dielectric targets can be easily deputed. The scattering characteristics of electrically large smooth Al and painted spheres are investigated in this paper, and the calculated RCS are verified by Mie scattering theory, the error is less than 0.1 dBm2. Based on lambert theory, it is demonstrated that the incoherent RCS is analyzed with better precision if the rough surfaces are divided into much more facets. In this paper, the coherent and incoherent scattering of rough Al and painted spheres are numerically observed, and the effects of surface roughness and materials are analyzed. The conclusions provide theoretical foundation for the terahertz scattering characteristics of electrically large rough targets.

The analysis of marine environmental parameters plays a significant role in various aspects, including sea surface target detection, the monitoring of the marine ecological environment, marine meteorology and disaster forecasting, and the monitoring of internal waves in the ocean. In particular, for sea surface target detection, the accurate and high-resolution input of marine environmental parameters is crucial for multi-scale sea surface modeling and the prediction of sea clutter characteristics. In this paper, based on the low-resolution wind speed, significant wave height, and wave period data provided by ECMWF for the surrounding seas of China (specified latitude and longitude range), a deep learning model based on a residual structure is proposed. By introducing an attention module, the model effectively addresses the poor modeling performance of traditional methods like nearest neighbor interpolation and linear interpolation at the edge positions in the image. Experimental results demonstrate that with the proposed approach, when the spatial resolution of wind speed increases from 0.5° to 0.25°, the results achieve a mean square error (MSE) of 0.713, a peak signal-to-noise ratio (PSNR) of 49.598, and a structural similarity index measure (SSIM) of 0.981. When the spatial resolution of the significant wave height increases from 1° to 0.5°, the results achieve a MSE of 1.319, a PSNR of 46.928, and an SSIM of 0.957. When the spatial resolution of the wave period increases from 1° to 0.5°, the results achieve a MSE of 2.299, a PSNR of 44.515, and an SSIM of 0.940. The proposed method can generate high-resolution marine environmental parameter data for the surrounding seas of China at any given moment, providing data support for subsequent sea surface modeling and for the prediction of sea clutter characteristics. [ABSTRACT FROM AUTHOR]