Achieving sub-10 m indoor ranging with LoRaWAN is difficult because multipath, human blockage, and micro-climate dynamics induce non-stationary attenuation in received signal strength indicator (RSSI) measurements. We present a lightweight, interpretable 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 (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 COST-231 multi-wall baseline (12.07 m MAE) and its environment-augmented variant (7.76 m MAE. Filtering reduces RSSI volatility from 10.33 to 5.43 dB and halves path loss error to 5.35 dB while raising R-squared from 0.82 to 0.89. The result is a single-anchor LoRaWAN ranging method with constant per-packet cost that is accurate, robust, and interpretable, providing a strong building block for multi-gateway localization.

翻译：利用LoRaWAN实现亚10米级室内测距具有挑战性，因为多径效应、人体遮挡及微气候动态变化会导致接收信号强度指示（RSSI）测量值产生非平稳衰减。本文提出一种轻量级、可解释的处理流程，将环境感知多墙路径损耗模型与仅前向、创新驱动的卡尔曼RSSI预滤波器相结合。该模型在距离和墙体衰减项基础上，引入了频率、信噪比（SNR）及同址环境协变量（温度、相对湿度、二氧化碳、颗粒物和大气压），并通过确定性反演进行距离估计。在包含超过200万条上行链路的单网关办公室一年期数据集上，该方法实现了距离估计的平均绝对误差（MAE）为4.74米，均方根误差（RMSE）为6.76米，优于COST-231多墙基线模型（12.07米MAE）及其环境增强变体（7.76米MAE）。滤波处理将RSSI波动从10.33 dB降低至5.43 dB，路径损耗误差减半至5.35 dB，同时将R平方值从0.82提升至0.89。最终形成了一种具有恒定单数据包计算成本、精确、鲁棒且可解释的单锚点LoRaWAN测距方法，为多网关定位系统提供了坚实的构建模块。