This work proposes a new indoor positioning system, named KLIP, that uses the K-means
clustering algorithm to split the environment into different sets of log-distance
propagation models in order to better characterize the indoor environment and further
improve the position estimation using Bayesian inference. The proposed method is
validated in a large-scale, real-world scenario composed of Bluetooth Low Energy
(BLE)-based devices. It is demonstrated, throughout the work, that the addition of
location information of training points to the received signal strength indicator (RSSI)
as an attribute for the clustering step improves the positioning accuracy. Moreover, the
obtained results show that the solution outperforms the naive Bayesian estimation up to
12% – regarding the positioning accuracy – and the broadly deployed kNN for reduced
training dataset size – regarding both accuracy and online processing time. In this sense,
KLIP proves to be an efficient and scalable alternative when both site-survey effort and
energy consumption constraints must be taken into account.
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