Reliable Indoor Location Prediction Using Conformal Measurement

Indoor localisation is the state-of-the-art to identify and observe a moving human or an object inside a building. However, because of the harsh indoor conditions, current indoor localisation systems remain either too expensive or not accurate enough. In this paper, we tackle the latter issue in a different direction, with a new conformal prediction algorithm to enhance the accuracy of the prediction. We handle the common indoor signal attenuation issue, which introduces errors into the training database, with a reliability measurement for our prediction. We show why our approach performs better than other solutions through empirical studies with two testbeds. To the best of our knowledge, we are the first to apply conformal prediction for the localisation purpose in general, and for the indoor localisation in particular.