<![CDATA[The instability of the Received Signal Strength Indicator (RSSI) signal is one of the causes of low accuracy in Bluetooth Low Energy (BLE)-based indoor navigation systems. This study aims to implement an Adaptive Kalman filter (AKF) as a technique for filtering RSSI signals in a 6x3 meter indoor environment using an experimental quantitative research method. Testing was conducted under unobstructed conditions and with obstructions in the form of people passing by. Filter performance was evaluated using standard deviation, while navigation accuracy performance was measured using Mean Error (ME) and Root Mean Square Error (RMSE). Test results show that AKF performs better than conventional KF under all tested conditions and is capable of reducing the average raw signal standard deviation by 64.0%-77.0% in unobstructed conditions and 64.4%-69.8% in obstructed conditions. In terms of position accuracy, AKF produced an average ME of 0.700 m and an RMSE of 0.742 m, with a 25.05% improvement in accuracy for ME and a 28.59% improvement for RMSE compared to the raw signal. This study demonstrates that AKF can be an effective filtering solution to improve RSSI signal quality in BLE-based indoor navigation systems.]]>
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