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 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.

Earth Observation spacecraft play a pivotal role in various critical applications impacting life on Earth. Historically, these systems have adhered to conventional operational paradigms, namely the “mow-the-lawn” and “bent pipe” approaches. In these paradigms, operational schedules are formulated on the ground and subsequently uploaded to the spacecraft for execution. Execution involves either systematically acquiring vast amounts of data (mow-the-lawn) or targeting specific areas of interest as defined by end users or operators. We aim to depart from these traditional methodologies by integrating onboard Artificial Intelligence, near real-time communication, and new observing strategies in one system called CogniSAT-6. These innovations will amplify the amount, speed, and quality of the information yielded by such a system by up to an order of magnitude. This paper provides an overview of the current state of the art in autonomous Earth Observation spacecraft and the application of onboard processing in Earth Observation spacecraft. An overview is given of the CogniSAT-6 mission, its concept of operations, system architecture, and data processing design. In addition, the first results of our in-orbit functional tests are presented. Since we believe that the technology presented here will have a significant impact on society, an ethical framework for such systems is presented. Finally, the benefits of the technology and implications for Earth Observation systems going forward are discussed.