发表机构
Graduate School of System Informatics, Kobe University（神户大学系统信息学研究科）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对室外稀疏BLE定位中的缺失数据，本文通过十天的牛群追踪实验证明填补空缺比保持时长更重要，且去噪优于重建，平滑强度存在最优值，离线方法增益有限。
AI 中文摘要
接收信号强度指示（RSSI）室外定位的锚点数量比其方法来源的室内系统少一到两个数量级。在本文报告的为期十天的牛群追踪部署中，四个网关覆盖了4,302平方米，即每1,000平方米有0.93个锚点。一头动物平均每秒被0.99个网关听到，因此给定秒的观测向量几乎从不完整。因此，如何填补空缺是首要的设计选择，而非预处理细节。填补本身价值8.1米，占保持时长设置所能带来改进的96%，并实现29%的误差减少。随后保持一个值多长时间占剩余4%的改进，从四秒到无限时长。一旦值可用，增益来自去除噪声而非重建丢失的样本：滤波后的信道估计优于保持的原始样本，而从未来样本向后填补则使其更差。正如该机制所预测，平滑强度存在一个真实的内点最优值，任一方向的偏离代价高昂。允许读取未来的平滑器仅获得0.55米的增益，仅为填补价值的十五分之一，这限制了任何离线方法所能增加的改进。三个方法族因结构性原因而非性能不佳在此不适用：四个信道中两个或更多仅在25%的秒内活跃，因此生成式插补必须学习的跨信道结构在很大程度上未被观测到。
英文摘要
Received signal strength indicator (RSSI) positioning outdoors has one to two orders of magnitude fewer anchors than the indoor systems its methods come from. In the ten-day cattle-tracking deployment reported here, four gateways cover $4{,}302\,\mathrm{m}^2$, or $0.93$ anchors per $1000\,\mathrm{m}^2$. An animal is heard by $0.99$ gateways per second on average, so the observation vector for a given second is almost never complete. How the gaps are filled is therefore a first-order design choice, not a preprocessing detail. Filling at all is worth $8.1\,\mathrm{m}$, $96\%$ of the improvement the hold-length setting can deliver and a $29\%$ error reduction. How long a value is then held is worth the remaining $4\%$, from four seconds to unbounded. Once a value is available, the gain comes from removing noise rather than rebuilding the lost sample: a filtered channel estimate improves on a held raw sample, whereas filling backwards from future samples makes it worse. As that mechanism predicts, smoothing strength has a real interior optimum that is costly to miss in either direction. A smoother allowed to read the future gains only $0.55\,\mathrm{m}$, one fifteenth of what filling is worth, which bounds what any offline method can add. Three method families do not apply here for structural reasons rather than poor performance: two or more of the four channels are live in only $25\%$ of seconds, so the cross-channel structure generative imputation must learn is largely unobserved.
Comments6 pages
