Improving BLE-Based Localization in Multipath-Affected Indoor Environments

Received signal strength indicator (RSSI)-based positioning with Bluetooth Low Energy (BLE) is widely used for indoor localization, and weighted centroid localization (WCL) is among the most common methods because of its simplicity and low computational cost. However, BLE RSSI is subject to temporal fluctuation and multipath interference, which induce an unstable RSSI-to-distance relationship and degrade the accuracy of conventional WCL. Its distance-based weighting cannot distinguish reliable line-of-sight (LOS) anchors from misleadingly strong non-line-of-sight (NLOS) anchors. This limitation is most severe in long, anchor-dense corridors and high-traffic lobbies, where multipath propagation is high. This paper presents a confidence-adaptive Kalman filtering framework for BLE RSSI-based localization in multipath-affected environments. A scan-level confidence score combines relative RSSI ranking, probabilistic LOS/PLOS/NLOS classification, and the proportion of high-confidence LOS predictions. The resulting score adapts the measurement covariance assigned to WCL position observations without modifying individual anchor weights. Relative to conventional WCL, the proposed method reduced the mean absolute error by 14.6% in corridors, 9.3% in a classroom, 3.3% in an office, and 7.9% along a high-traffic lobby trajectory. Relative to a fixed-covariance Kalman filter, the additional reduction was small in the low-traffic zones (0.4–2.0%) and largest in the high-traffic lobby (7.7%).