A novel multi-sensor and multi-topological database for indoor positioning on fingerprint techniques

In fingerprinting-based indoor positioning systems, Received Signal Strength (RSS) values are collected at predetermined reference points to construct a fingerprint map. A well-established fingerprint database plays an important role in positioning, especially enhancing positioning accuracy. In literature, there are studies that consider only one type of measurements such as Wi-Fi or Bluetooth RSS, but these values are not sufficient alone to overcome the problems in dynamically changed environments. In order to deal with this, we propose a novel fingerprint database that contains both Wi-Fi and Bluetooth RSS values in addition to magnetic field measurements obtained from mobile devices. In addition to this, the proposed database also contains Wi-Fi, Bluetooth (BT) and Bluetooth Low Energy (BLE) RSS values obtained from preplaced sensor nodes in the experimental environment. The aims of this fingerprint database are to enhance accuracy, precision, and robustness of the location estimation system to dynamically changed environment and to satisfy researchers' needs who are deal with different problems in indoor positioning.