Gaussian Mixture Modeling for Wi-Fi fingerprinting based indoor positioning in the presence of censored data

Trung KienVu1*, Hung Lan Le2

1Faculty of Electronics, Hanoi University of Industry

2National Center for Technological Progress

Received 4 September 2018; accepted 6 December 2018



In complex indoor environments, due to the attenuation of the signal and the changing surrounding environment, the censoring and multi-component problems may be present in the observed data. Censoring refers to the fact that sensors on portable devices cannot measure Received Signal Strength Index (RSSI) values below a specific threshold, such as -100 dBm. The multi-component problem occurs when the measured data varies due to obstacles and user directions, whether the door is closed or open, etc. By accounting for these problems, this paper proposes to model the RSSI probability density distributions using the Censoring Gaussian Mixture Model (C-GMM) and develop the Expectation-Maximization (EM) algorithm to estimate the parameters of this model in the offline phase of the Wi-Fi fingerprinting based Indoor Positioning Systems (IPS). The simulation results demonstrate the effectiveness of the proposed method.

Keywords: censored data, EM algorithm, fingerprinting, Gaussian Mixture Model, IPS.

Classification numbers: 1.3, 2.3