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.
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http://dx.doi.org/10.3390/s20092579 | DOI Listing |
Sensors (Basel)
November 2021
School of Aeronautics and Astronautics, Zhejiang University, Hangzhou 310013, China.
A lost-in-space star identification algorithm based on a one-dimensional Convolutional Neural Network (1D CNN) is proposed. The lost-in-space star identification aims to identify stars observed with corresponding catalog stars when there is no prior attitude information. With the help of neural networks, the robustness and the speed of the star identification are improved greatly.
View Article and Find Full Text PDFJ Opt Soc Am A Opt Image Sci Vis
August 2021
A star tracker, in lost-in-space (LIS) and tracking operation modes, applies an accurate algorithm in the star identification phase. The pattern-matching-based star identification algorithms apply patterns to search a prebuilt database. By applying this newly proposed database, it is possible to apply many LIS algorithms in the LIS and tracking modes.
View Article and Find Full Text PDFSensors (Basel)
June 2020
Intel Corporation, Intel R&D Ireland Ltd, Collinstown, Collinstown Industrial Park, Co. Kildare, W23 CX68, Ireland.
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.
View Article and Find Full Text PDFSensors (Basel)
May 2020
Aerospace Engineering, Texas A&M University, College Station, TX 77843, USA.
This study introduces a new "Non-Dimensional" star identification algorithm to reliably identify the stars observed by a wide field-of-view star tracker when the focal length and optical axis offset values are known with poor accuracy. This algorithm is particularly suited to complement nominal lost-in-space algorithms, which may identify stars incorrectly when the focal length and/or optical axis offset deviate from their nominal operational ranges. These deviations may be caused, for example, by launch vibrations or thermal variations in orbit.
View Article and Find Full Text PDFSensors (Basel)
May 2020
Intel Corporation, Intel R&D Ireland Ltd., Collinstown, Collinstown Industrial Park, Co. Kildare, W23CW68 Collinstown, Ireland.
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.
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