用环境感知模型和卡尔曼滤波，提升室内LoRaWAN定位精度至4.7米。
Environment-Aware Indoor LoRaWAN Ranging Using Path Loss Model Inversion and Adaptive RSSI Filtering
- 融合温湿度等环境参数的多墙路径损耗模型，反演距离
- 在百万级数据上实现4.74米平均误差，优于基线12.07米
- 轻量可解释，适合已校准的室内部署场景
在室内环境中，多径效应、人体遮挡和微气候变化导致接收信号强度（RSSI）非平稳衰减，使实现亚10米的LoRaWAN定位极具挑战。本文提出一种轻量、可解释、现场校准的流水线，将环境感知的多墙路径损耗模型与前向、创新驱动的卡尔曼预滤波器结合。该模型引入频率、信噪比（SNR）及共置环境协变量（温度、相对湿度、二氧化碳、颗粒物、气压），并进行确定性反演以估计距离。基于包含超过200万次上行传输的一年期单网关办公环境数据集，方法在距离估计中达到4.74米均值绝对误差（MAE）和6.76米均方根误差（RMSE），优于仅结构的COST-231多墙基线（12.07米MAE）以及无滤波的环境增强变体（7.76米MAE）。滤波将RSSI波动从10.33 dB降至5.43 dB，路径损耗均方根误差从8.09 dB降至5.35 dB，决定系数R²由0.82提升至0.89。结果为单锚点LoRaWAN定位提供每包开销O(1)的稳定、可解释且实用方案，可作为未来多网关定位的基石和室内LoRaWAN定位的基准。
原文摘要 · Abstract (English)
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 present a lightweight, interpretable, site-calibrated pipeline that couples an environment-aware multi-wall path loss model with a forward-only, innovation-driven Kalman prefilter for RSSI. The model augments distance and wall terms with frequency, signal-to-noise ratio (SNR), and co-located environmental covariates, including temperature, relative humidity, carbon dioxide, particulate matter, and barometric pressure, and is inverted deterministically for distance estimation. On a one-year single-gateway office dataset comprising over 2 million uplinks, the approach attains a mean absolute error (MAE) of 4.74 m and a root mean square error (RMSE) of 6.76 m in distance estimation, improving over a structure-only COST-231 multi-wall baseline with 12.07 m MAE and an environment-augmented variant without filtering with 7.76 m MAE. Filtering reduces RSSI volatility from 10.33 to 5.43 dB and lowers the path loss RMSE from 8.09 to 5.35 dB, while increasing R^2 from 0.82 to 0.89. The result is a single-anchor LoRaWAN ranging method with O(1) per-packet cost that is stable, interpretable, and practical within a calibrated indoor deployment, providing a useful building block for future multi-gateway localization and a benchmark for indoor LoRaWAN ranging.
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