An Improved Satellite ISAL Imaging Vibration Phase Compensation Algorithm Based on Prior Information and Adaptive Windowing

Spaceborne inverse synthetic aperture ladar (ISAL) can achieve high-resolution imaging of satellite targets. However, because the amplitudes of satellite microvibration are comparable to the ladar wavelength, the echoes will contain both space-variant and space-invariant phase errors. These errors will lead to azimuthal image defocus and impede target analysis and identification. In this paper, we establish a phase error estimation model based on satellite vibration characteristics. Based on this model, we propose a vibration phase error compensation algorithm using prior information and adaptive windowing. Compared to conventional algorithms, this algorithm utilizes prior information to improve estimation accuracy while significantly reducing computational complexity. Furthermore, high-accuracy phase function estimation can be achieved through maximum likelihood estimation and adaptive window filtering, thereby enabling the compensation of vibration phase errors. Both simulation and real imaging experiments validate the effectiveness and robustness of the proposed algorithm.