Adaptive probabilistic model using angle of arrival estimation for IoT indoor localization

The industrial demands for accurate localization systems have been rapidly increasing after the introduction of the Internet of Things (IoT) concept. Self localization and tracking transmitting sources are considered essential parts of IoT applications. In this paper we studied the possibility of applying angle of arrival (AoA) estimations to localize an IoT transceiver device in an indoor environment. Furthermore, we propose an adaptive probabilistic model which works on top of the AoA estimation technique to improve the localization accuracy. The experimental results show the potential of using AoA-based localization for indoor environments. The results furthermore show that the proposed adaptive probabilistic model outperforms the traditional static probabilistic model in terms of the localization accuracy and the stability of the position estimate.