Reliable positioning is essential for Uncrewed Aerial Vehicles (UAVs) in safety-critical urban operations, yet achieving sub-meter accuracy under stringent latency constraints remains challenging. While 3rd Generation Partnership Project (3GPP) specifies repeated Positioning Reference Signals (PRS) transmissions for accurate Time Difference of Arrival (TDoA) measurements, denoising techniques specifically tailored for extremely limited measurement sequences within 3GPP frameworks remain underexplored. We propose Adaptive Gain Exponential Smoother (AGES), a lightweight filter combining exponentially weighted averaging with adaptive gains informed by 3GPP measurement quality reports. Simulations demonstrate AGES achieves 30-40% reduction in positioning error with only 3-5 repeated measurements while maintaining Fifth Generation New Radio (5G-NR) infrastructure compatibility.

翻译：可靠定位对于安全关键型城市作业中的无人机（UAV）至关重要，但在严格延迟约束下实现亚米级精度仍具挑战性。尽管第三代合作伙伴计划（3GPP）规定了重复定位参考信号（PRS）传输以实现精确到达时间差（TDoA）测量，但针对3GPP框架内极端有限测量序列定制的去噪技术仍未得到充分探索。我们提出自适应增益指数平滑器（AGES），这是一种轻量级滤波器，将指数加权平均与基于3GPP测量质量报告的自适应增益相结合。仿真表明，在保持第五代新无线电（5G-NR）基础设施兼容性的同时，AGES仅需3-5次重复测量即可实现定位误差降低30-40%。