Advanced positioning systems are required to overcome the limitations of conventional GNSS in challenging environments such as urban canyons, semi-indoor areas, and multipath-rich regions. Signal degradation, NLOS reception, and severe multipath significantly reduce accuracy and reliability. To address these limitations, a next-generation positioning framework integrating LFBOC-based GNSS signal acquisition, AI-based NLOS satellite classification, satellite power system modeling, and high-precision navigation and localization is essential.
This research aims to build an Advanced Positioning System that ensures robust and accurate performance in difficult environments by combining high-resolution GNSS signal processing, environment-adaptive AI classification, and reliable satellite power system modeling.
Research Competitiveness
LFBOC-based signal acquisition provides sharper correlation characteristics and broader effective bandwidth than traditional BPSK/BOC, enabling superior detection performance under low-power and noisy conditions.
AI-based NLOS classification accurately identifies multipath and non-line-of-sight satellites, addressing the dominant source of GNSS errors in dense urban environments far more effectively than rule-based methods.
Accurate satellite power system modeling enhances the stability and continuity of satellite signal availability, strengthening overall service reliability.
By integrating these components into a unified system, the proposed research delivers higher accuracy, robustness, and environmental adaptability compared to existing GNSS technologies.
