Multi-Layer LEO Constellation Optimization for GLONASS Augmentation: Geometric Complementarity and Rapid PPP Convergence

Résumé fourni par la source

The GLONASS constellation suffers from inherent geometric weaknesses in mid–low latitude regions, where uneven satellite elevation distribution leads to degraded PDOP and limits high-precision positioning performance. To address this gap, this paper proposes a three-layer heterogeneous LEO augmentation architecture, jointly optimized using an adaptive gradient-based framework and a CVaR-augmented objective to enhance geometric robustness under both average and worst-case conditions. The optimized design demonstrates significant performance improvements. For a 180-satellite configuration, the global mean PDOP is reduced from 2.31 to 1.39 (40.0% improvement), while polar-region PDOP improves by over 50%. More importantly, the proposed multi-layer LEO architecture substantially enhances Precise Point Positioning (PPP) convergence, reducing the convergence time from 27.6 min to 2.8 min, achieving a 9.8× acceleration. This improvement can be primarily attributed to the complementary multi-altitude and multi-inclination geometry, which significantly increases satellite visibility and strengthens observation diversity. These results highlight the effectiveness of heterogeneous multi-layer LEO constellations in overcoming the intrinsic limitations of legacy GNSSs. The proposed framework provides a scalable and robust design paradigm for future LEO-PNT systems, with direct implications for next-generation high-precision navigation services in challenging environments.

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