Slow-Spinning Spacecraft Cross-Range Scaling and Attitude Estimation Based on Sequential ISAR Images

Attitude estimation for space targets based on inverse synthetic aperture radar (ISAR) is essential for various on-orbit operations, including spacecraft status analysis and fall forecast. However, traditional attitude estimation methods based on ISAR images suffer from poor feature extraction performance. Furthermore, the present methods can only deal with stabilized spacecraft. In situations where a noncooperative spacecraft is in a slow-spinning state, the ISAR cross-range scaling is inaccurate, resulting in incorrect attitude estimation. In this study, we propose a cross-range scaling and attitude estimation method that can be applied to slow-spinning spacecraft. The method transforms attitude estimation to a parameter optimization task, where the optimization model aims to recover the cross-range scaling factor and attitude. First, the slow-spinning state is parameterized and the optimization model of cross-range scaling and attitude estimation is established. Second, semantic features are extracted from ISAR images by the proposed multiscale semantic feature extraction network. The optimization model is then solved through semantic features using the proposed robust estimation algorithm. Ultimately, the correct cross-range scaling factor and attitude of the corresponding ISAR image are acquired. This article makes the following three contributions: a specialized mathematics model to perform cross-range scaling and attitude estimation of slow-spinning model is proposed; by substituting traditional feature points with semantic features, the reliability of ISAR feature extraction is improved; and the proposed robust estimation algorithm enhances the accuracy of parameter estimation. Experiments demonstrate that this method has higher cross-range scaling and attitude estimation accuracy for slow-spinning spacecraft than usual methods.