Investigation into the Imaging Characteristics of Rough Targets with Inverse Synthetic Aperture Lidar

Inverse Synthetic Aperture Lidar (ISAL) technology surpasses the optical diffraction limit, achieving centimeter-level resolution for ultra-long-distance radar imaging. This study delves into the ISAL imaging of rough surfaces, employing rough surfaces as models to effectively replicate the intricacies of complex real-world environments.The study employs the Monte Carlo method to construct the geometric model of rough surfaces, providing a comprehensive analysis of parameter selection for such surfaces. Leveraging the Kirchhoff approximation method, the scattering echo characteristics of rough surfaces are meticulously calculated and thoroughly examined.Subsequently, the convolution back-projection algorithm was applied to simulate ISAL imaging for surfaces with varying roughness parameters. The results demonstrate that lower surface roughness enhances the proportion of coherent scattering, rendering the target edges sharper and more defined, whereas higher roughness leads to a more uniform energy distribution across the reconstructed image. This study establishes a comprehensive simulation framework, encompassing environmental modeling, electromagnetic modeling, and ISAL imaging techniques for rough surface targets. The simulation results reveal that rough surface targets exhibit distinctive scattering characteristics in ISAL imaging, providing a robust theoretical foundation for remote sensing and parameter inversion in natural environments.