English(EN) Environment-Aware Indoor LoRaWAN Ranging Using Path Loss Model Inversion and Adaptive RSSI Filtering

研究人员开发环境感知LoRaWAN路径损耗模型以提高室内测距精度

作者 PulseAugur 编辑部 · [2 个来源] · 2026-05-05 04:00

研究人员开发了一种利用LoRaWAN技术并通过考虑环境因素来提高室内测距精度的新方法。该方法整合了一个考虑温度、湿度和空气质量等变量以及信号强度的路径损耗模型。该模型结合自适应滤波，显著降低了测距误差，在一项研究中实现了4.74米的平均绝对误差。 AI

影响提高了物联网设备的室内定位精度，可能实现更精确的基于位置的服务和改进的网络性能。

排序理由该集群包含两篇arXiv论文，详细介绍了无线通信领域的新研究方法。[lever_c_demoted from research: ic=2 ai=0.4]

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

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arXiv cs.LG TIER_1 English(EN) · Nahshon Mokua Obiri, Kristof Van Laerhoven · 2026-05-05 04:00

基于路径损耗模型反演和自适应RSSI滤波的面向环境的室内LoRaWAN测距

arXiv:2505.01185v3 Announce Type: replace-cross Abstract: Achieving sub-10 m indoor ranging with LoRaWAN is challenging because multipath, human blockage, and micro-climate dynamics induce non-stationary attenuation in received signal strength indicator (RSSI) measurements. We pr…
arXiv cs.LG TIER_1 English(EN) · Nahshon Mokua Obiri, Kristof Van Laerhoven · 2026-05-05 04:00

环境感知室内 LoRaWAN 路径损耗：参数回归比较、阴影衰落和校准衰落裕度

arXiv:2510.04346v2 Announce Type: replace-cross Abstract: Indoor long range wide area network (LoRaWAN) propagation is shaped by structural and time-varying environmental factors, which limit single-slope log-distance models and the standard log-normal shadowing assumption. We pr…