Forward Modeling of Scattering Centers From Coated Target on Rough Ground for Remote Sensing Target Recognition Applications

• Published in: IEEE transactions on geoscience and remote sensing 2024, Vol.62, p.1-17
• Main Authors: He, Siyuan, Hua, Mengbo, Zhang, Yunhua, Du, Xiaoyong, Zhang, Fan
• Format: Article
• Language: English
• Subjects: Analytical models; Coating target; Coatings; Computation; Computational modeling; Echoes; forward modeling of scattering centers (SCs); Mathematical models; Modelling; Parameters; Radar; Radar detection; Remote sensing; remote sensing and recognition applications; rough ground; SAR (radar); Scattering; scattering characteristics; Statistical analysis; Statistical methods; Synthetic aperture radar; Target detection; Target recognition; Three dimensional models
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2023.3344128

This article presents a forward modeling method for the scattering center (SC) model of coated targets on rough ground, tailored for remote sensing and target recognition applications. The methodology initiates with the geometric model and coating impedance parameters of the ground target, employing a forward approach to decompose overall scattering into multiple component-level sources. Integrating high-frequency electromagnetic (EM) theory, quantitative computation, and derivation of SC parameters constructs a parameterized 3-D model for coated targets on rough ground. Illustrated through the example of a T72 tank and considering the effects of coated media and ground coupling, a forward SC model for the tank target on rough ground is developed. The model's statistical characteristics are meticulously analyzed, and its accuracy is validated using real measurement data from publicly available datasets. The proposed model offers several advantages. First, it directly reflects the local geometry and coated material characteristics of target components, providing detailed information about component shapes and materials. Second, the modeling process meticulously considers and segregates the contributions of the target's complex coupled SCs with the ground. A statistical analysis demonstrates consistent parameter convergence, aligning with theoretical expectations. Third, this research extends the forward modeling approach to coated targets on rough ground, enabling the model not only for component-level identification but also for rapid target synthetic aperture radar (SAR) echo reconstruction. In conclusion, this cost-effective approach provides a practical means for the rapid estimation and analysis of radar characteristics, target detection and recognition, target remote sensing, and other applications in complex environments.