The study of composite scattering characteristics from target and environment is of great significance in the fields of target detection and identification in complex backgrounds. In this paper, typical environments such as canyons, earth-sea junctions, and islands are modelled by combining a composite environment modelling approach on top of the traditional stochastic rough surface generation. Subsequently, the multiple coupled scattering paths of the target and environment are analysed on the basis of the target and environment scattering calculation, with the objective of achieving the electromagnetic scattering calculation of the target and the composite environment. The numerical results demonstrate that the composite scattering model proposed in this paper meets the expected results in terms of solution accuracy and computational efficiency.
In this paper, a method of environmental geometric modeling utilizing a digital elevation model is proposed to solve the problem that the environmental modeling based on spectral function cannot describe the actual terrain. At the same time, by combining the shooting and bouncing ray (SBR) method with the facet-based scattering model (FBSM), the SBR-FBSM algorithm is proposed. Finally, the simulation analysis is carried out for the actual environment, the target environment composite scene, and the complex scene respectively to verify the rationality, and effectiveness of the proposed electromagnetic scattering algorithm and its applicability in the SAR imaging field.
In order to improve the detection capability of typical non-cooperate targets, a facet-based synthetic aperture radar (SAR) imaging algorithm, and a SAR image target detection model are presented in this paper. At first, the shooting and bouncing ray (SBR) method was utilized to calculate the backscattering coefficient of each facet on the typical target surface. Then, based on the radar echo generation method and SAR imaging algorithm, the SAR images of the targets can be obtained by simulation. Therefore, a SAR image dataset can be established containing simulation results under different conditions. Finally, combined with the most recently proposed YOLOv7 deep learning model, the feature learning and training based on the target SAR dataset are realized. Compared with the previous original YOLOv5 and improved YOLOv5 networks, experimental results show that YOLOv7 performs better in precision and efficiency under the same conditions, which provides a concrete foundation for future research.
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