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Developing star trackers quickly is non-trivial. Achieving reproducible results and comparing different algorithms are also open problems. In this sense, this work proposes the use of synthetic star images (a simulated sky), allied with the standardized structure of the Universal Verification Methodology as the base of a design approach. The aim is to organize the project, speed up the development time by providing a standard verification methodology. Future rework is reduced through two methods: a verification platform that us shared under a free software licence; and the layout of Universal Verification Methodology enforces reusability of code through an object-oriented approach. We propose a black-box structure for the verification platform with standard interfaces, and provide examples showing how this approach can be applied to the development of a star tracker for small satellites, targeting a system-on-a-chip design. The same test benches were applied to both early conceptual software-only implementations, and later optimized software-hardware hybrid systems, in a hardware-in-the-loop configuration. This test bench reuse strategy was interesting also to show the regression test capability of the developed platform. Furthermore, the simulator was used to inject specific noise, in order to evaluate the system under some real-world conditions.

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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-inthe-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.

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