INDOOR POSITIONING WITH BLUETOOTH LOW ENERGY AND EXTENDED KALMAN FILTER

Tadei-Nazarii Kalynchuk

Abstract


Background. Indoor positioning systems based on Bluetooth Low Energy (BLE) beacons widely rely on estimating distance using the received signal strength indicator (RSSI). However, RSSI measurements in indoor environments are significantly affected by multipath propagation, shadowing, interference, and absorption by obstacles, resulting in high variability of signal strength and substantial distance estimation errors. The nonlinear logarithmic relationship between RSSI and distance further complicates the application of conventional linear filtering techniques such as the classical Kalman Filter, which requires prior transformation of measurements and may lead to loss of optimality.

Materials and Methods. This study proposes a distance estimation method based on the Extended Kalman Filter (EKF), which directly processes RSSI measurements using the nonlinear log-distance path loss model. The experiment was performed in an indoor office environment using two Silicon Labs EFR32BG22 BLE beacons and a Nordic nRF52840 receiver. The EKF parameters were selected based on prior calibration of the propagation model coefficients.

Results and Discussion. The experimental results demonstrate that the EKF effectively smooths RSSI. For the beacon with lower RSSI dispersion, the root mean square error (RMSE) reached 0.14 m, for the second beacon, the RMSE was 0.53 m. The analysis confirms that estimation accuracy strongly depends on signal stability and calibration quality. Compared to direct RSSI-to-distance conversion and the classical Kalman Filter approach reported in related work, the EKF-based algorithm reduces the mean absolute distance estimation error by approximately 20–30%, validating the advantages of nonlinear filtering.

Conclusion. The proposed EKF-based method improves the accuracy and robustness of RSSI-based distance estimation in BLE indoor positioning systems. When model parameters are properly calibrated, the achieved accuracy is sufficient for practical applications such as smart building navigation, asset tracking, and robotic localization. The algorithm can be implemented on resource-constrained embedded platforms
and serves as a foundation for further development of multisensor indoor positioning systems.

Keywords: positioning, Bluetooth, BLE, RSSI, Kalman filter, Extended Kalman Filter


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References


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DOI: http://dx.doi.org/10.30970/eli.33.3

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