Robust RSSI-Based Indoor Positioning System Using K-Means Clustering and Bayesian Estimation

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. Our proposed method is validated in a large-scale, real-world scenario composed of Bluetooth Low Energy (BLE)-based devices. We demonstrate, throughout the paper, 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 our solution outperforms the naive Bayesian estimation up to 12% – regarding the positioning accuracy – and the broadly deployed
${k}$
NN 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.