Akademik Veri Yönetim Sistemi
Advances in Space Research, 2026 (SCI-Expanded, Scopus)
Deep-space spacecraft navigation primarily relies on ground-based radiometric tracking, supplemented by onboard optical observations of celestial bodies when necessary. As autonomous navigation becomes increasingly critical for reducing communication delays and ground station dependency, optimizing the observed beacons requires balancing estimation accuracy against computational efficiency. While existing approaches focus on either minimal optimal pair selection or comprehensive all-beacon utilization, a systematic methodology for determining sufficient beacon counts that balance performance and computational constraints remains absent. This study develops a threshold-based adaptive beacon selection framework using the Cramér-Rao Lower Bound to quantify theoretical performance limits across different beacon configurations. The methodology is evaluated using a representative deep-space trajectory spanning several years with nine major celestial bodies. Optimal beacon subsets are identified through combinatorial evaluation, with position estimation performed using the Linear Optimal Sine Triangulation algorithm and performance validation conducted through Monte Carlo analysis. Results demonstrate that optimal beacon pairs achieve only a fraction of the maximum theoretical accuracy, while incorporating additional beacons substantially reduces position error variance, with each successive beacon contributing progressively less improvement. The adaptive approach with a performance threshold criterion maintains navigation accuracy approaching the all-beacons case while requiring fewer measurements and reducing computational load. Computational analysis reveals that exhaustive combinatorial search exceeds the cost of direct all-beacon processing, though pre-computation during mission planning phases enables practical implementation. This framework provides a systematic approach to specify desired accuracy levels relative to theoretical limits, enabling informed decisions on beacon selection strategies for resource-constrained autonomous spacecraft systems.