Inter-pulse time-varying vibration compensation with a physically-informed deep neural network for synthetic aperture Ladar imaging

Abstract

Synthetic Aperture Ladar (SAL) provides high-resolution, high-data-rate, and detailed imaging for remote sensing. However, its short wavelength makes SAL systems highly sensitive to vibrations, introducing Doppler frequency shifts and range cell migration that degrade image quality, particularly for extended targets. Traditional vibration compensation methods often face limitations in challenging scenes with severe vibration conditions or when strong scattering points are absent. To address these challenges, a second-order vibration error model is firstly developed to characterize the time-varying errors within each tunable period. Then, a physically-informed deep neural network is designed to estimate the vibration coefficients through its encoder, which are then used by physical layers in the decoder to correct the errors. By combining the physical model with a data-driven approach, the proposed method can mitigate severe vibration-induced errors and reduce range cell migration without relying on strong scattering points. Additionally, integration of the physical layers makes the decoder being non-parametric, thus simplifies the network training. Numerical results validate the method’s effectiveness and its superiority over traditional spectral correlation algorithm, demonstrating its potential for high-resolution SAL imaging.