Multi-Sensor Fusion for Indoor Positioning System

Diverse sources of radio signal interference and the complex topology of multi-level indoor structures often impede reliable position tracking. Therefore, a solution based on a Pedestrian Dead-Reckoning (PDR) navigation module utilizing high-frequency data from Inertial Measurement Unit (IMU), as well as two complementary technologies: Bluetooth Low Energy (BLE) – employing RSSI-based distance estimation and Ultra-Wideband (UWB), which relies on Time-of-Flight (ToF) signal, was proposed. The continuous, high-frequency motion tracking provided by the IMU was periodically corrected using absolute, though inherently noisy, radio-based measurements. This fusion strategy allowed for the mitigation of drift accumulation, yielding a trajectory estimate that remains both locally smooth over short intervals and globally accurate over extended durations. An Extended Kalman Filter (EKF) served as the fusion mechanism, integrating relative motion estimates from the PDR with absolute positional updates to perform a series of controlled experiments under diverse conditions. The analysis of the collected data revealed that the standalone PDR system exhibited a substantial drift error on average between 10% and 15% of the total traversed distance. Sensor fusion with BLE measurements significantly reduced this error, achieving a localization accuracy of Root Mean Square Error (RMSE) = 0.98 m. Under analogous conditions, the UWB-based system demonstrated a decisive advantage, reaching an accuracy level of RMSE = 0.21 m, corresponding to nearly a fivefold improvement compared to BLE. The experimental results confirm that UWB, when combined with sensor fusion frameworks, provides a balance among positional accuracy and robustness to environmental disturbances, making it effective for precise indoor localization.