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Title: Centroid determination hardware algorithm for star trackers

Authors: Gabriel Mariano Marcelino; Victor Hugo Schulz; Laio Oriel Seman; Eduardo Augusto Bezerra

Addresses: Space Technology Research Laboratory, Federal University of Santa Catarina (UFSC), Florianópolis, SC, 88040-900, Brazil ' Space Technology Research Laboratory, Federal University of Santa Catarina (UFSC), Florianópolis, SC, 88040-900, Brazil ' Applied Computer Science Master Program, Itajaí Valley University (UNIVALI), Itajaí, SC, 88302-901, Brazil; Department of Computer Engineering, Federal University of Technology – Paraná (UTFPR), Apucarana, PR, Brazil ' Space Technology Research Laboratory, Federal University of Santa Catarina (UFSC), Florianópolis, SC, 88040-900, Brazil

Abstract: The execution of centroid extraction algorithms using a microprocessor consumes considerable resources when compared to the other steps involved in star trackers. This paper presents a method to identify star centroids in star trackers by pre-processing the pixels using a field-programmable gate array (FPGA) directly in the stream transmitted by an image sensor. The dedicated hardware filters the star pixels and transmits them to a processor, which computes the centroids of the respective image using an infinite impulse response filter. Thus, there is a substantial decrease in memory consumption and a reduction of the processor usage during the attitude determination computation, making the process more attractive for small satellites. A hardware-in-the-loop simulation is presented to test the performance of the system. It was possible to achieve a subpixel precision in the centroid coordinates' estimation, and also lower execution times in comparison with methods based on the processing of whole images.

Keywords: embedded systems; nanosatellites; attitude determination; star trackers; centroid determination; FPGA; field-programmable gate array; hardware acceleration; image processing; navigation systems; star sensors.

DOI: 10.1504/IJSNET.2020.104458

International Journal of Sensor Networks, 2020 Vol.32 No.1, pp.1 - 14

Received: 27 Jun 2019
Accepted: 01 Aug 2019

Published online: 12 Jan 2020 *

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