Key-Feature-Assisted Joint Dynamic Estimation and 3-D Reconstruction for Spaceborne ISAR Imaging of Fixed-Axis Spinning Target

Space situational awareness (SSA) faces critical challenges in characterizing the dynamics and structures of fixed-axis spinning space objects using spaceborne inverse synthetic aperture radar (ISAR). The performance of existing methods depends on high-quality 2-D imaging and precise feature extraction, which suffer from 3-D spatial migration and phase errors under complex relative motion. To address the aforementioned challenges, we propose a joint dynamic estimation and 3-D reconstruction framework based on key-feature-assisted Doppler coefficients analysis. After modeling the complex relative motion of the imaging system and analyzing the imaging characteristics of the satellite target, we establish an overdetermined equation that describes the connections between Doppler coefficients and target parameters. Our framework is as follows. First, we introduce a hierarchical multiorder Doppler coefficients estimation method combining coarse estimation and stabilized Doppler estimation via 2-D alternating direction method of multipliers (SDE-2-D-ADMMs). Second, we leverage spatial distribution characteristics of Doppler coefficients to extract planar features without image segmentation. Third, we employ a magnitude-assisted Kalman filter (MA-KF) for robust scatter-point association and solve overdetermined equations via an optimization algorithm for rapid rotational parameter estimation and 3-D surface reconstruction. Experimental validation across multiple spin states and orbital conditions demonstrates superior performance in rotational velocity estimation and 3-D surface reconstruction.