A Parallel Artificial Neural Network Learning Scheme Based on Radio Wave Fingerprint for Indoor Localization

Radio wave fingerprinting is known to be the best method for indoor positioning, and its performance depends greatly on the data comparison algorithm that is used. This paper implements a radio wave fingerprint positioning method with artificial neural network learning to improve the performance of a conventional radio fingerprint positioning algorithm based on the Euclidean distance. We propose a parallel learning method to reduce the error in the indoor height and an indoor positioning data augmentation method for data generalization. This method exhibits a higher performance than an existing Euclidean distance based positioning method. In particular, the data augmentation technique can be applied without depending on the specific positioning algorithm.