Sept. 1, 2016, 6 a.m. UTC
Sept. 1, 2017, 6 a.m. UTC
Above are the final results published after the algorithms were profiled on our architecture: Intel(R) Xeon(R) CPU E5-2650 0 @ 2.00GHz, cache size : 20480 KB
The list above includes all teams who submitted a solution that could be scored with respect to accuracy. Only the top two teams also submitted the necessary code to measure the algorithm speed and the result clearly show that maaf1980 designed the algorithm with the highest accuracy and speed, while Multi-Poles_Algorithm is following closely with their approach.
An international team including researchers from the Instituto Nacional de Pesquisas Espaciais (INPE, São José dos Campos, Brazil), the Centro Universitario de la Defensa - Zaragoza (CUD, Spain), and Texas A&M University (TAMU, College Station, USA). Their new algorithm is called “Super k-ID”.
Members of the teams and contributors to the Super k-ID were:
1) Dr. Marcio Fialho (INPE), who led the effort by coding, debugging, testing, and improving Super k-ID to the winning version.
2) Mr. David Arnas (CUD), who developed optimal combinatory kernel sequences, making the algorithm very robust.
3) Dr. Christian Bruccoleri (TAMU), who provided a large experience on Star-ID algorithms.
4) Dr. Daniele Mortari (TAMU), doctorate advisor of Dr. Fialho and Dr. Bruccoleri, who developed the k-vector technique, making the algorithm very fast.
The team belongs to the Automation, Robotics and Control for Aerospace lab (ARCAlab) of the School of Aerospace Engineering, “Sapienza” University of Rome, Italy. The team published the original idea of the proposed approach in Advances in Space Research, Vol. 59, Issue 8, Pages 2133-2147, “A novel star identification technique robust to high presence of false objects: The Multi-Poles Algorithm ( https://doi.org/10.1016/j.asr.2017.01.034). The Multi Poles Algorithm (MPA) was modified and improved for the ESA contest.
The team conducts research activities for the use of star trackers in harsh environments (interplanetary missions, low-thrust orbit raising from LEO to GEO) and to exploit the star sensor images in estimating high angular rates. For these purposes, the team developed a high-fidelity simulator to test star identification algorithms.
The researchers of the team and the contribution of each member for the contest were:
1) Vincenzo Schiattarella (Research Assistant) modified and improved the MPA, making the algorithm faster. Moreover, he provided the coding, debugging and testing of the algorithm.
2) Dario Spiller (PhD student), shared his expertise on star trackers and optimization algorithms.
3) Fabio Curti (Associate Professor), director of ARCAlab, provided his knowledge and skills on star identification and attitude estimation algorithms.