Enhanced Position Estimation via RSSI Offset Correction in BLE Fingerprinting-Based Indoor Positioning

Jingshi Qian, Nobuyoshi Komuro (), Won-Suk Kim and Younghwan Yoo
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Jingshi Qian: Graduate School of Science and Engineering, Chiba University, 1-33, Yayoi-cho, Inage-ku, Chiba 263-8522, Japan
Nobuyoshi Komuro: Chiba University Digital Transformation Enhancement Council, Chiba University, 1-33, Yayoi-cho, Inage-ku, Chiba 263-8522, Japan
Won-Suk Kim: Division of Computer Science and Engineering, Pusan National University, 2 Busandaehak-ro 63 beon-gil, Geumjeong-gu, Busan 43241, Republic of Korea
Younghwan Yoo: Division of Computer Science and Engineering, Pusan National University, 2 Busandaehak-ro 63 beon-gil, Geumjeong-gu, Busan 43241, Republic of Korea

Future Internet, 2025, vol. 17, issue 10, 1-19

Abstract: Since GPS (Global Positioning System) cannot meet accuracy requirements indoors, indoor Location-Based Services (LBSs) have become increasingly important. BLE (Bluetooth Low Energy) offers cost and accuracy advantages. Typically, the position fingerprinting method is used for indoor positioning. However, due to irregular reflection and absorption, the indoor environment introduces various offsets in Bluetooth RSSI (Received Signal Strength Indicator). This study analyzed the RSSI space and proposed a pre-processing workflow to improve position estimation accuracy by correcting offsets in RSSI space for BLE fingerprinting methods using machine learning. Experiments performed using different position estimation methods showed that the corrected data achieved a 6% improvement over the filter-only result. This study also evaluated the effects of different pre-processing and post-processing filters on positioning accuracy. Experiments were also conducted using a published dataset and showed similar results.

Keywords: indoor positioning; RSSI; Bluetooth low energy; position fingerprinting method (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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