A Robust Indoor Positioning Framework Leveraging CSI and RSSI Fingerprints in IEEE 802.11n Wireless Networks

ABSTRACT CSI and RSSI fingerprints are applied in this paper to produce a robust indoor localization framework within IEEE 802.11n wireless networks. This system employs a two‐phase fingerprinting approach consisting of calibration and positioning to operate effectively in complex indoor environments. A detailed radio map is created using advanced CSI processing and aggregation followed by a probabilistic location estimation based on correlations. Through the use of multiple testbeds, such as the IT‐1 and IT‐2 buildings, the proposed model is evaluated against traditional methods including RSSI, FIFS, and CSI‐MIMO. As compared to existing approaches, the proposed model consistently outperforms them in indoor settings. In light of these findings, the location‐based indoor services provided by the system are of high precision.