Platform Vibration Fine Modeling and Adaptive Motion Compensation Algorithm for Terahertz SAR Imaging

Terahertz synthetic aperture radar (SAR) exhibits broad application prospects due to its capability for highresolution imaging and detailed target extraction. However, its short wavelength makes terahertz SAR extremely susceptible to the platform vibration, leading to many issues during the imaging process such as false imaging points, azimuthal blur-ring, and defocused SAR images. Therefore, this paper establishes a fine platform vibration model of terahertz SAR, and proposes an adaptive terahertz SAR motion compensation algorithm. Based on the impact mechanism analysis of the plat-form vibration on imaging using the mathematical model, the complex platform vibration in terahertz SAR imaging scenes can be compensated flexibly and accurately. Firstly, a fine terahertz SAR vibration model is established based on the tempo-ral amplitude modulation vibration model (TAMVM). By integrating the cosine time-varying amplitude and the random time-varying amplitude modulation vibration model, the TAMVM model reduces the limitation of the traditional harmonic model, and improves the adaptability to the complex and variable terahertz SAR platform vibration. Secondly, to address the performance loss of traditional harmonic model-based motion compensation algorithms when handling the complex plat-form vibration, this paper proposes an adaptive motion compensation method based on the Levenberg-Marquardt (LM) algo-rithm under the minimum Tsallis entropy criterion. The image quality-driven motion compensation algorithm proposed in this paper does not rely on the dominant target points, and it can precisely estimate the complex and varying vibration phase under the nonlinear least squares framework without the additional compensation steps. Moreover, the iterative process of the LM algorithm is derived under the minimum Tsallis entropy criterion in this paper. This algorithm adaptively adjusts the search displacement to achieve the feedback update and the iterative optimization, enabling precise estimation of the vibra-tion phase and suppression of image blur, thereby obtaining high-quality focused terahertz SAR images. Furthermore, the comparison results of the simulated and real-measured data verify the rationality and feasibility of the proposed TAMVM model, and demonstrate the superiority of the proposed adaptive motion compensation method in achieving the precise tera-hertz SAR image focusing and suppressing false imaging points.