Efficient Star Identification Using a Neural Network.

The required precision for attitude determination in spacecraft is increasing, providing a need for more accurate attitude determination sensors. The star sensor or star tracker provides unmatched arc-second precision and with the rise of micro satellites these sensors are becoming smaller, faster and more efficient. The most critical component in the star sensor system is the lost-in-space star identification algorithm which identifies stars in a scene without a priori attitude information. In this paper, we present an efficient lost-in-space star identification algorithm using a neural network and a robust and novel feature extraction method. Since a neural network implicitly stores the patterns associated with a guide star, a database lookup is eliminated from the matching process. The search time is therefore not influenced by the number of patterns stored in the network, making it constant (O(1)). This search time is unrivalled by other star identification algorithms. The presented algorithm provides excellent performance in a simple and lightweight design, making neural networks the preferred choice for star identification algorithms.

A Survey of Lost-in-Space Star Identification Algorithms since 2009.

The lost-in-space star identification algorithm is able to identify stars without a priori attitude information and is arguably the most critical component of a star sensor system. In this paper, the 2009 survey by Spratling and Mortari is extended and recent lost-in-space star identification algorithms are surveyed. The covered literature is a qualitative representation of the current research in the field. A taxonomy of these algorithms based on their feature extraction method is defined. Furthermore, we show that in current literature the comparison of these algorithms can produce inconsistent conclusions. In order to mitigate these inconsistencies, this paper lists the considerations related to the relative performance evaluation of these algorithms using simulation.

The Sileye-3/Alteino experiment for the study of light flashes, radiation environment and astronaut brain activity on board the International Space Station.

In this work we describe the instrument Sileye-3/Alteino, placed on board the International Space Station in April 2002. The instrument is constituted by an Electroencephalograph and a cosmic ray silicon detector. The scientific aims include the investigation of the Light Flash phenomenon, the measurement of the radiation environment and the nuclear abundance inside the ISS and the study of astronaut brain activity in space when subject to cosmic rays.