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Advisor(s)
Abstract(s)
Indoor localization systems enable object tracking, e.g., related to retail, logistics and mobile robotics during their life cycle. This paper describes a real-world scenario implementation, based on Bluetooth Low Energy (BLE) beacons, evaluating a hybrid indoor positioning system (H-IPS) that combines two RSSI-based approaches: Multilateration (MLT) and Fingerprinting (FP). In addition, a Kalman Filter (KF) was employed to
decrease the positioning errors of both techniques. Furthermore a track-to-track fusion (TTF) is performed on the two KF outputs to maximize the performance. The results show that the accuracy of H-IPS overcomes the standalone FP in 21%, while the original MLT is outperformed in 52%. Finally, the proposed solution demonstrated a probability of error < 2 m of 80%, while the same probability for the FP and MLT were 56% and 20%, respectively.
Description
Keywords
Fingerprinting Multilateration Indoor positioning system Kalman filtering Sensor fusion Bluetooth low energy
Citation
de Oliveira, Leonardo; Rayel, Ohara; Leitao, Paulo (2022). Hybrid indoor localization system combining multilatration and fingerprinting. In the 48th Annual Conference of the IEEE Industrial Electronics Society (IECON’22). Brussels. p. 1-6
Publisher
IEEE