Indoor Localization with Transfer Learning

Abstract

Indoor positioning methods aim to estimate positions of transmitters where the GPS signals are unavailable. These systems usually employ algorithms explicitly trained for a single location such as fingerprinting method. For that reason, they can only be used in a particular location. This restriction prevents the use of the fingerprint method in tasks such as search and rescue operations where there is no prior knowledge of the place. A fingerprinting system using a trained algorithm with data collected from many places can work in multiple places. This paper proposes an indoor positioning system that uses the parameters of a pre-trained neural network trained with the data obtained from finite difference time domain simulations with transfer learning without collecting large amounts of data. The initial parameters for the model to be trained with the received signal strength (RSS) data collected from real places are used as be the parameters of the artificial neural network trained with the aforementioned simulation data. Performance results of the trained model are comparable to the results of the works in which fingerprinting method is employed in a single environment.