Abstract
Fingerprinting technique for indoor positioning based on 5G system has attracted attention. Kalman filter (KF) is used as preprocessing of raw data to reduce the disturbance of Received Signal Strength (RSS) values. After preprocessing, Universal Kriging (UK) algorithm is adopted to reduce the efforts of establishing a fingerprinting database by Spatial Interpolation. A machine learning algorithm named K -Nearest Neighbour (KNN) is used to calculate user equipment’s position. Real experiments are setup with 5G signals over the air. Two indoor scenarios are considered depending whether the base station is located in the same room with user equipment or not. In test room A, the proposed KF and UK algorithms achieve 53% positioning accuracy improvement. In test room B, 43% performance improvement is obtained by the proposed algorithm. 1.44-meter positioning error is observed as the best case for 80% test samples.
Highlights
Global Navigation Satellite System (GNSS) has provided enough accuracy for outdoor positioning but not good indoor. 5G Internet of Things (IoT) is a popular research topic including various application scenarios such as indoor positioning, smart transportation, smart manufacturing, and smart security [1,2,3,4]
Reference Signal Receiving Power (RSRP), Received Signal Strength (RSS), Sounding Reference Signal (SRS) and other signals are used for positioning [13,14,15]
Indoor positioning is an indispensable part of human life in the future
Summary
Global Navigation Satellite System (GNSS) has provided enough accuracy for outdoor positioning but not good indoor. 5G Internet of Things (IoT) is a popular research topic including various application scenarios such as indoor positioning, smart transportation, smart manufacturing, and smart security [1,2,3,4]. 5G New Radio (NR) continues to evolve to further enhance LTE performance [8,9,10,11,12]. RSS-based positioning system includes a radio propagation distance loss model and fingerprinting method [16, 17]. The radio propagation distance loss model requires multiple BSs to perform trilateral positioning and applies in simple environments, while it is not easy to observe multiple NR BSs in a room in the early deployment phase. Fingerprinting technique includes offline and online stages. NR, RSS, and coordinates of each reference point are extracted to form fingerprints and input into a fingerprinting database. The RSS of the test point is measured in real time and compared with the offline fingerprints to calculate positions.
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