A feedback star identification algorithm via regularized pattern recognition using a unique feature extraction

Abstract

This thesis presents a star identification algorithm integrated with preprocessing. Star sensors, which are highly reliable for attitude determination use of spacecrafts and satellites, relies on star identification algorithms. The star identification algorithm proposed in this study is capable of functioning either in lost-in-space method or recursive method. Both methods utilize a unique feature extraction scheme. This novel approach of feature extraction method extracts a single vector from each captured image instead of treating each star as a separate object. This cumulative approach aims to save a significant amount of memory space while taking the entire catalog into account for elevated accuracy. A database containing stars from the catalog is constructed using the unconventional features extracted from each corresponding field-of-view. The databases may differ in size and detail dependent on the parameters of overlapping ratio and brightness threshold. These parameters have a significant effect on accuracy and complexity of the method. The method aims to estimate the inertial boresight vector and the rotation angle about it. This is a novel approach that is carried out by matching frames but not matching individual stars, star pairs, star triangles or star polygons. Both star identification methods rely on pattern recognition and regularization successively. First, a 1NN classifier is used to perform a coarse estimation with limited accuracy specified by the characteristics of the database with predetermined parameters. The coarse estimation is exactly the database vector that is most similar to the observation vector. Subsequently, a dictionary is generated using the neighbor database vectors of the most similar database vector. The final estimation is obtained by conducting a regularization method for fine estimation. A solution coefficient vector is yielded through regularization. The estimates of boresight vector and rotation angle are retrieved using the solution coefficient vector. This is the output of the lost-in-space star identification method. Since the lost-in-space algorithm is very sensitive to false stars, an additional false star filtering algorithm is developed. This algorithm is based on density-based clustering. A disparity list is created using two successive image frames. After estimating true stars by implementing density-based clustering on the disparity list, false stars are removed. Using the successive frames containing only estimated true stars, an affine transformation matrix is obtained by a regression analysis procedure. In order to overcome the issues tackling the lost-in-space star method, the recursive star identification method is developed. Apart from the algorithmic structure taken from the lost-in-space method, it possesses an update mechanism that ensures usage a much smaller portion of the database to reduce computational complexity and average run time. Also, the false star filtering avoids sensitivity to false stars.