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With the increasing of on-orbit satellites, target dynamic monitoring becomes more and more important in space situation awareness applications. Currently, there are some exploratory methods monitoring attitude-stabilized targets using various high-resolution remote sensing technologies. However, it is still a big challenge to achieve dynamic estimation of spin satellite with single sensor. Inspired by the existing matching based works, this article presents a dynamic estimation method of spin satellite using single-station inverse synthetic aperture radar (ISAR) images. When the angle accumulation is set to be a constant in each ISAR imaging period, the projection change of target structures in the long-term observation sequence is described with the hidden Markov model to build the mathematic expression of target on-orbit state. In this condition, target sequential attitude can be solved with Viterbi decoding algorithm even the azimuth scaling of each image is difficulty due to target spin motion. Then, combined with the radar observation geometry, target sequential attitude parameters are substituted into a spin parameter optimization, which is solved by particle swarm optimization algorithm. In the end, target instantaneous attitude vectors and spin speed are used to express its dynamic during the observation period. Simulation experiments of a typical spin spacecraft, Tiangong-I, confirm the feasibility of the proposed algorithm, and its superiority is also investigated by comparison with the existing work. [ABSTRACT FROM AUTHOR]

This paper introduces a new concept of slow-time spatial frequency space, also known as “slow-time $k$ -space,” and redefines the associated cross-range bandwidth, which arises naturally from Doppler radar tomography. The slow-time $k$ -space is the most suitable for narrow-band radar imaging, and can be augmented for higher resolution imaging by appropriate signal processing. The respective relationships with the more traditional concepts of fast-time $k$ -space and signal (or range) bandwidth are explained. We also discuss image resolution limits and demonstrate the concepts with simulated data examples. [ABSTRACT FROM AUTHOR]