An algorithm for three-dimensional indoor positioning based on Bayesian inference, Fingerprinting method and Wi-Fi technology

Wireless indoor positioning systems have been shown to be very useful in many applications and have been the subject of a considerable amount of research, mainly concerning the two-dimensional (2D) case. However, in many practical situations it is necessary to determine the three-dimensional (3D) coordinates of an object or user. In this paper, a hybrid algorithm for implementation in a 3D indoor positioning system is proposed. This algorithm is implemented by using a fingerprinting technique based on both the k-means and naive Bayes methods, and uses the received signal strength (RSS) as an input parameter. In addition, a comparison of the main algorithms discussed in previous research papers and the proposed algorithm is presented. Indoor positioning experiments were conducted in a typical building with two floors (180m2) and four access points (APs). The proposed algorithm exhibited a better performance than that of other algorithms, with a mean error around 1.80m.