A Robust Star Identification Algorithm Based on a Masked Distance Map

The authors of this paper propose a robust star identification algorithm for a 'Lost-In-Space'-mode star tracker for lost-cost CubeSat missions. A two-step identification framework and an embedded validation mechanism were designed to accelerate the process. In the first step, a masked distance map is designed to provide a shortlist of stars, and the embedded fast validation process enables the direct output of validated stars before the second step. In the second step, local similarity is utilized to select a set of stars from those shortlisted, and the final validation procedure rejects all unsatisfactory stars. This algorithm can provide reliable and robust recognition even when the captured star images include severe star positioning errors, missing stars and false stars. The proposed algorithm was verified by a simulation study under various conditions. As low-cost star sensors face harsh and unknown environments during deep space CubeSat missions such as asteroid exploration, the proposed algorithm with high robustness will provide an important function.