Joint 3-D ISAR Imaging and Dynamic Estimation of Spinning Spacecraft Based on Signal Phase Analysis

Three-dimensional (3-D) geometry reconstruction and dynamic estimation of spinning spacecraft are crucial for space surveillance. Existing methods rely on interpreting 2-D inverse synthetic aperture radar (ISAR) images, which often suffer from image defocusing and scaling errors induced by target spin. To tackle this problem, we propose a joint 3-D ISAR imaging and dynamic estimation framework based on signal phase analysis, eliminating the need for prior 2-D imaging. After modeling the complex rotation of a spinning spacecraft relative to radar, we establish an overdetermined system of equations based on relationships between target parameters and cubic phase coefficients of radar echoes. Then, our two-step solution framework is organized as follows. First, we estimate cubic phase coefficients of the multicomponent signal through sparse representation (SR). To construct compact dictionaries for SR, we combine the coarse estimation and the phase order hierarchical processing (POHP) strategy to guide their design. Second, 3-D locations of scatterers, spin velocity, and direction of spin axis are estimated from the phase coefficients by solving the equation system. To ensure accurate and efficient solutions, we derive their iterative formulas using an alternating iterative strategy. With these formulas, 3-D ISAR imaging and dynamic estimation can be achieved directly. The effectiveness and superiority of our method are verified by experiments on simulated ISAR echoes.