© 2019 CEUR Workshop Proceedings. All rights reserved.Accurate estimation of the radio frequency maps is key in practical indoor localization, but this requires dense sampling of the electromagnetic field, which is also known as fingerprinting. To decrease the time-consuming fingerprinting process, we both aim to estimate probabilistic radio frequency maps (RM) using artificial neural networks (ANN), and automatically pick the most informative fingerprint positions following an active learning strategy based on uncertainty sampling, aided by Gaussian Processes (GP). Once we have an estimate of the RM of the environment, the problem can be formulated as a tracking problem with a Hidden Markov Model (HMM), and the RM can be used as the observation densities of the HMM. The trajectories of sequential positions are approximated with a sequential Monte Carlo filter. The results indicate that fingerprint measurements can be reduced by 30% in return of 8.1% decrease in the localization performance.