A High-Quality Airborne THz-SAR Imaging Framework Based on Extended Vibration Reconstruction

Terahertz synthetic aperture radar (THz-SAR) provides high spatial resolution and low latency, offering significant potential for advanced remote sensing applications. However, motion compensation in THz-SAR requires extremely high precision, while current methods often struggle to maintain stable performance in complex scenes, which limits their practical deployment. This paper presents an imaging framework for airborne THz-SAR based on extended vibration reconstruction and feature-adaptive autofocus, termed EvrFA. The framework first utilizes the redundancy and periodicity of resonant signals to perform preliminary correction of echo data. A vibration model is then constructed within local azimuth segments and extended over the entire sub-aperture, enabling effective suppression of subtle high-frequency vibrations through signal reconstruction. Finally, a feature-adaptive autofocus algorithm is proposed to compensate for residual phase errors. Experiments were conducted using five real airborne THz-SAR datasets acquired by a system operating in the 220 GHz band, covering a variety of terrain types. The results demonstrate that the proposed framework achieves higher compensation accuracy than several state-of-the-art imaging algorithms. Owing to its high compensation accuracy and efficient focusing performance, the EvrFA framework is suitable for integration into terahertz video synthetic aperture radar (Vi-SAR) imaging systems.