RSS Signal Modeling-Based Rapid and Accurate Fingerprinting Database Construction of Indoor Localization Technology

Le résumé fourni par la source

This paper proposes an RF signal modeling technology that can quickly and easily construct a fingerprinting database for Bluetooth beacon signals in indoors. Fingerprinting technology estimates a location by comparing the measured Received Signal Strength (RSS) with the database where RSS is stored for each indoor location. So, the fingerprinting technology is robust to multipath problem, but has the disadvantage that it takes a lot of time and cost to construct the fingerprinting database. The proposed technology consists of the following three processes: (1) beacon arrangement, (2) signal distribution modeling, and (3) model update using measurements. The beacon arrangement process is a process of setting the location of the beacon in coordinate system. The signal distribution modeling process is a process of calculating the RSS value according to the travel distance by computing the signal propagation path from the set beacon location to each indoor location. To reflect signal propagation characteristics (diffraction and penetration) on the propagation model, the proposed technology computes a propagation path of the signal using the A-Star algorithm. It computes signal propagation path using the structure information of indoor map, so it cannot reflect all RF signal changes due to structures and materials in indoors. It utilizes measurements in some areas of the indoor space to correct RSS distribution for the entire indoor space. To verify the performance of the proposed technology, we have performed localization tests by installing 9 Bluetooth beacons on one floor of a building consisting of many pillars and wide halls. When only the modeling result was used as a fingerprinting database, the Root Mean Square Error (RMSE) was 8.1 dBm. After the measurement update, the RMSE was 4.9 dBm. Through this, it was confirmed that the proposed technology can provide high accuracy while significantly reducing time and manpower required for fingerprinting database construction.

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