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.

翻译：实现基于LoRaWAN的亚10米级室内测距极具挑战性，原因在于多径效应、人体遮挡及微气候动态变化会导致接收信号强度指示（RSSI）测量值呈现非平稳衰减。本文提出一种轻量级、可解释且支持现场标定的流水线方案，该方案将环境感知的多墙路径损耗模型与仅前向、创新驱动的卡尔曼预滤波器相结合，用于处理RSSI信号。模型在距离项和墙体项基础上，引入频率、信噪比（SNR）及协同定位环境协变量（包括温度、相对湿度、二氧化碳浓度、颗粒物浓度和大气压），并通过确定性反演实现距离估计。基于涵盖一年时长的单网关办公数据集（包含超过200万次上行链路数据），该方法在距离估计中取得了4.74米的平均绝对误差（MAE）和6.76米的均方根误差（RMSE），相较于仅使用结构的COST-231多墙基线模型（MAE=12.07米）及未滤波的环境增强变体（MAE=7.76米）均有显著提升。滤波处理将RSSI波动性从10.33分贝降低至5.43分贝，路径损耗均方根误差从8.09分贝降至5.35分贝，同时将R^2从0.82提升至0.89。最终形成一种单锚点LoRaWAN测距方法，其计算复杂度为每个数据包O(1)，在标定后的室内部署环境中具有稳定性、可解释性和实用性，为未来多网关定位系统提供了基础构建模块，并为室内LoRaWAN测距建立了基准参考。