Improved triangular-based star pattern recognition algorithm for low-cost star trackers

Abstract

Star identification algorithms based on triangular-pattern are more suitable for low-cost star trackers since they require less star density in the field of view to operate effectively. In this paper, we propose a modified star pattern recognition algorithm based on the triangular-based algorithm of “LIEBE”. The main contribution of the proposed work is twofold. First, a new strategy for the selection of star triplets is proposed for database construction. Second, new selection criteria of the reference star are considered for pattern generation process. A sky simulation program is developed to assess mainly the robustness against different conditions of noise. The obtained results show an improvement in the overall identification rate, more robustness towards missing stars, and more efficiency towards magnitude noise. Furthermore, our proposed algorithm shows comparable robustness with the recently proposed triangular algorithms despite their reliance on more accurate camera and a validation process. To assess the algorithm performance, the algorithm is implemented on a prototype of Data Processing Unit (DPU) based on ARM Cortex-M4 processor. In this part, we discuss the major design decisions and we present the hardware architecture of DPU. The algorithm shows promising running time at a reduced on-board database when implemented on ARM platform.