Transfer Learning for Fingerprint-based Indoor Localization Calibration

The fingerprint - based indoor localization (FIL) method has good localization accuracy. It performs indoor localization by collecting beacon signals of beacon nodes at different reference points to form beacon fingerprints, where beacon nodes are pre-deployed at specific locations. To improve the localization accuracy when FIL methods are applied to new application domains, exemplified by those using new beacon signal receiving devices, it is necessary to perform localization calibration. This paper proposes a transfer learning indoor localization calibration (TL-ILC) scheme based on weight-freezing of the transfer learning concept. The proposed scheme needs to collect only a small amount of data to fast retrain the artificial neural network used by the FIL methods. Experiments show that TL-ILC can indeed perform localization calibration to achieve good localization accuracy.