Indoor Fingerprint Positioning Method Based on Real 5G Signals

Indoor positioning services are being used more and more widely. However, existing indoor positioning techniques cannot simultaneously take into account low cost, ease of use, high precision, and seamless switching between indoor and outdoor positioning. With the maturity of 5G techniques, 5G-based indoor positioning is gradually being paid attention to. 5G-based indoor positioning does not require additional equipment, and supports flexible indoor and outdoor switching under the same system. However, the 5G-related information used in existing research on 5G indoor positioning is not open to users. Therefore, in this paper, we propose an indoor fingerprint positioning method based on measured 5G signals. This method first collects 5G signals in the positioning area, and processes them to form a fingerprint database. Then, a machine learning algorithm is used to match the signal to be located with the fingerprint database to obtain the positioning result. Finally, we conduct experiments in real field, and the experimental result demonstrates that the positioning accuracy of our proposed method can reach 96%.