Reverse Attitude Statistics-Based Star Map Identification Method

The star sensor is generally affected by the atmospheric background light and the aerodynamic environment when working in near-space, which results in missing stars or false stars. Moreover, high-speed maneuvering may cause star trailing, which reduces the accuracy of the star position. To address the challenges for star map identification, a reverse attitude statistics-based method is proposed. Conversely to existing methods that match before solving for attitude, this method introduces attitude solving into the matching process and obtains the final match and the correct attitude simultaneously by frequency statistics. First, based on stable angular distance features, the initial matching is obtained using spatial hash indexing. Then, the star pairs are accurately matched by applying the attitudes’ frequency statistics method. In addition, Bayesian optimization is used to find optimal parameters to enhance the algorithm performance. In this work, the proposed method is validated in simulation, field test, and on-orbit experiment. Compared with the state-of-the-art, the identification rate is improved by more than 14.3%, and the solving time is reduced by over 28.5%.