An Infrared Star Identification Algorithm Based on Ordered Angular Distance Verification

Short-wave infrared star sensors have become a key technology for all-time attitude determination within the atmosphere, in which the star identification algorithm plays a fundamental role. However, due to the limited number of detectable stars in infrared images, achieving robust and accurate identification remains challenging. To address this issue, this paper proposes a star identification algorithm based on ordered angular distance verification. The algorithm first extracts radial and adjacency features via full-field-of-view sorting to mitigate the impact of “edge loss”. It then employs a fast initial matching that combines hash table lookup with binary search, substantially reducing the number of candidate navigation stars requiring detailed matching. Subsequently, a local search matching procedure corrects index misalignment caused by false or missing stars, while angular distance invariance verification prevents false matches; the combination of these mechanisms significantly enhances the algorithm’s robustness. In simulations using 5000 star images, the proposed algorithm achieves an identification rate of 99.48%. It maintains a rate above 96% under position noise, magnitude noise, and false stars. The average processing time per star image is 10.57 ms, approximately 39% of that required by the conventional grid algorithm (27.01 ms). The simulation results demonstrate that the proposed algorithm achieves high identification accuracy and maintains strong robustness in complex noise environments.