###### Abstract  
To meet the requirements for radar echo acquisition and feature extraction from stratified media and rough-surfaced targets, a vehicle was geometrically modelled in CAD. Monte Carlo techniques were applied to generate the rough interfaces at air-snow and snow-soil boundaries and over the vehicle surface. Soil complex permittivity was characterized with a four-component mixture model, while snow permittivity was described using a mixed-media dielectric model. The composite electromagnetic scattering from a rough-surfaced vehicle on snow-covered soil was then analyzed with the finite-difference time-domain (FDTD) method. Parametric studies examined how incident angle and frequency, vehicle orientation, vehicle surface root mean square (RMS) height, snow liquid water content and depth, and soil moisture influence the composite scattering coefficient. Results indicate that the coefficient oscillates with scattering angle, producing specular reflection lobes; it increases monotonically with larger incident angles, higher frequencies, greater vehicle RMS roughness, and higher snow liquid water content. By contrast, its dependence on snow thickness, vehicle orientation, and soil moisture is complex and shows no clear trend. composite electromagnetic scattering; time-domain finite-difference method; layered ground surface; CAD modeling; Monte Carlo method 2025  
81 July 202519 July 202 method to investigate electromagnetic wave propagation in various moving structures. [PERSON] et al. [10] focused on the two-dimensional electromagnetic scattering from multiple inhomogeneous, arbitrarily anisotropic scatterers embedded in a multilayered biaxially anisotropic elliptical cylinder. [PERSON] et al. [11] examined electromagnetic scattering and inverse scattering problems of isotropic and anisotropic two-dimensional inhomogeneous targets across multiple planar layers by improving the hybrid finite-element-boundary-integral (FEBI) method. [PERSON] et al. [12] derived a theoretical model to calculate magnetic field transmission characteristics in ocean environments for submarine cable detection and analyzed the attenuation behavior in seawater, establishing a corresponding cable simulation model. [PERSON] et al. [13] proposed a target-oriented adaptive frequency-domain finite-element method (FEM) to address electromagnetic radiation problems involving complex structures. [PERSON]. [PERSON] et al. [14] proposed a deep convolutional learning network for solving the electromagnetic backscatter problem of buried conductors. In the aforementioned studies, the targets are mostly assumed to have smooth and ideal surfaces. However, such surfaces are rarely encountered in real-world scenarios, where targets often undergo deformation or exhibit surface roughness. In the studies on composite electromagnetic scattering, the targets considered generally possess relatively simple geometries, and the corresponding geometric modeling methods are also relatively straightforward. However, real-world targets are often irregular in shape and have surface roughness, an aspect that has not been addressed in the above research [15]. Similarly, the backgrounds in these studies are mostly simplified as rough surfaces composed of two media, leading to correspondingly simplified background modeling. The background may also consist of complex stratified media. Therefore, the geometric modeling of both the target and the background becomes significantly more challenging. To address this challenge, this study introduces, for the first time, a method that integrates CAD modeling software (AutoCAD 2024) with Python-assisted techniques to construct a vehicle model with surface roughness. Leveraging Python (Python 3.12.3), the vehicle surface can be flexibly configured to represent both geometric deformations and varying roughness patterns. As a demonstration, a simulation experiment is conducted in which the vehicle surface roughness is generated according to the Monte Carlo algorithm.