Performance Enhancement of Wi-Fi Fingerprinting-Based IPS by Accurate Parameter Estimation of Censored and Dropped Data

In complex indoor environments, the censoring, dropping, and multi-component problems may present in the observable data. This is due to the attenuation of signals, the unexpected operation of equipments, and the changing surrounding environment. Censoring refers to the fact that sensors on portable devices are unable to measure Received Signal Strength Index (RSSI) values below a certain threshold, for example, −100 dBm with typical smart phones. Dropping means that, occasionally, RSSI measurements of Wifi access points are not available, although their value is clearly above the censoring threshold. The multi-component problem occurs when the measured data varies due to obstacles as well as user directions; doors closed or open; and so forth. Taking these problems into consideration, this paper proposes a novel approach to enhance the performance of the Wifi Fingerprinting based Indoor Positioning System (WF-IPS). The proposed method is verified through simulated data and real field data. The experimental results show that our proposal outperforms the other state-of-the-art WF-IPS approach both in positioning accuracy and computational cost.