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Area of Science:

  • Space Science and Technology
  • Computer Science and Engineering
  • Navigation Systems

Background:

  • Mega low Earth orbit (LEO) constellations offer global navigation advancements.
  • Increasing satellite numbers create bottlenecks in real-time satellite selection.
  • Current methods face challenges balancing computational speed and geometric performance.

Purpose of the Study:

  • To develop an efficient and stable algorithm for real-time satellite selection in LEO navigation.
  • To address the limitations of existing exhaustive search and heuristic algorithms.
  • To improve the geometric performance and temporal stability of satellite selection.

Main Methods:

  • Proposed a density-clustered fast stable selection algorithm.
  • Identified high-density regions in celestial sphere distributions.
  • Employed hybrid geometric optimization and a Forward Stable Strategy for reduced handovers.

Main Results:

  • Achieved near-optimal geometric precision within millisecond computation times.
  • Demonstrated significantly reduced satellite handover frequency in continuous tracking.
  • Validated robust performance across various scenarios including occlusion and parameter variations.

Conclusions:

  • The proposed algorithm effectively balances computational efficiency and geometric performance for LEO navigation.
  • Density clustering initialization and geometric optimization are crucial for algorithm effectiveness.
  • Provides a viable solution for real-time satellite selection challenges in LEO systems.