Effective fingerprinting database construction through digital map-based RF signal modeling and partial measurements in indoor environments

Abstract

This paper presents a radio-frequency (RF) signal modeling technology that builds a fingerprinting database for indoor localization quickly and accurately. Fingerprinting-based localization technology uses location-specific signal characteristics as a database; therefore, it is less sensitive to multipath problems. The proposed approach predicts signal propagation paths and calculates attenuation based on an indoor map, reducing infrastructure installation and data collection time. Because the indoor map lacks accurate information about all structures, the modeling results contain errors when compared to measurements. To address this, measurements from a partial area improve modeling accuracy by accounting for received signal strength changes caused by indoor structures. In experiments with seven beacons, the proposed database construction method achieves an average error of 5.16 dBm and a localization error of 1.61 m, comparable to the 1.14-m error in measurement-based databases, while reducing database construction time by 41.06%. These results demonstrate the effectiveness of the proposed technology in rapidly and accurately building databases for indoor localization.