Environment-Aware Indoor LoRaWAN Ranging Using Path Loss Model Inversion and Adaptive RSSI Filtering

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.