Reverse Attitude Statistics Based Star Map Identification Method

TL;DR

This paper introduces a novel star map identification method that integrates attitude solving into the matching process, improving accuracy and speed in challenging near-space conditions through frequency statistics and Bayesian optimization.

Contribution

It proposes a reverse attitude statistics based approach that simultaneously solves for attitude and star matching, enhancing robustness against noise, false stars, and missing stars.

Findings

Identification rate increased by over 14.3%

Matching time reduced by more than 28.5%

Validated through simulation, field test, and on-orbit experiments

Abstract

The star tracker 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 starmap identification, a reverse attitude statistics based method is proposed to handle position noise, false stars, and missing stars. Conversely to existing methods which 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. Firstly, based on stable angular distance features, the initial matching is obtained by utilizing spatial hash indexing. Then, the dual-vector attitude determination is introduced to calculate potential attitude. Finally, the star pairs are accurately matched by applying a frequency statistics filtering method. In addition, Bayesian optimization is employed to find optimal parameters under the impact of noises, which is able to enhance the algorithm performance further. 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%.