A lost-in-space star identification algorithm based on regularized pattern recognition

Abstract

Accuracy in spacecraft attitude determination has been improved continuously in parallel to scientific payloads requiring higher accuracy. Star trackers aim to maintain attitude determination by matching observations to the reference stars. They provide very high accuracy at any point in space at cost of higher computational complexity. This study presents an algorithm of lost-in-space star identification with high accuracy and acceptable computational complexity. This task is achieved by means of a novel dictionary-based star matching method by applying nearest neighbor classification through a binary search of Euclidean distances and regularization sequentially, accompanied by use of unconventional feature vectors representing each observation individually. The proposed method yields an estimated direction of the boresight vector orthogonal to the camera plane with an estimated axial rotation. The Hipparcos catalog is used for database generation, and the sensor parameters are taken from the project SharjahSat-1. The performance is evaluated in consideration of noise including position, brightness and false stars. By means of complexity analysis of database size and average run time, it is shown that the proposed method achieves better accuracy than other methods and maintains very high robustness to position noise and brightness noise. A high level of accuracy is achieved with an amount of database size and average run time that are competitive in the recent literature. It is demonstrated that the levels of pointing accuracy offered by state-of-the-art ADCS models are achieved by the proposed method with the given probabilities.