Bluetooth Based Indoor Localization Using Triplet Embeddings

We propose a novel algorithm for indoor localization using triplet embeddings through Bluetooth connectivity streams obtained in very noisy settings with irregular sampling schemes using environmental sensors distributed ad hoc inside buildings. We pose the problem as a matrix completion problem, where a single row and column is added to the (noisy) distances matrix of sensors. Since this is an underdetermined problem, we use information from connectivity between the sender and the trackers to find the missing distances with the help of triplet comparisons. We test our algorithm in a busy hospital setting, where locations such as patient rooms, intensive care units and nursing stations have been equipped with Bluetooth trackers, that are capable of sending Bluetooth packets as well. We assign the sender role to three different trackers, and estimate their locations. We achieve a mean error of 4.01m in indoor localization for three different senders.