A Fingerprint Method for Indoor Localization Using Autoencoder Based Deep Extreme Learning Machine

By growing the demand for location based services in indoor environments in recent years, fingerprint based indoor localization has attracted much research interest. The fingerprint localization method works based on received signal strength (RSS) in wireless sensor networks. This method uses RSS measurements from available transmitter sensors, which are collected by a smart phone with internal sensors. In this article, we propose a novel algorithm that takes advantage of deep learning, extreme learning machines, and high level extracted features by autoencoder to improve the localization performance in the feature extraction and the classification. Furthermore, as the fingerprint database needs to be updated (due to the dynamic nature of environment), we also increase the number of training data, in order to improve the localization performance, gradually. Simulation results indicate that the proposed method provides a significant improvement in localization performance by using high level extracted features by autoencoder and increasing the number of training data.