An environment-augmented path loss model inverted for distance plus Kalman RSSI prefiltering yields 4.74 m MAE and 6.76 m RMSE for indoor LoRaWAN ranging on a large single-gateway dataset.
Machine-Learning-Based Combined Path Loss and Shadowing Model in LoRaW AN for Energy Efficiency Enhancement
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Environment-Aware Indoor LoRaWAN Ranging Using Path Loss Model Inversion and Adaptive RSSI Filtering
An environment-augmented path loss model inverted for distance plus Kalman RSSI prefiltering yields 4.74 m MAE and 6.76 m RMSE for indoor LoRaWAN ranging on a large single-gateway dataset.