Improving the Performance of Deep Learning for Wireless Localization

Abstract

Indoor localization systems are most commonly based on Received Signal Strength Indicator (RSSI) measurements of either WiFi or Bluetooth-Low-Energy (BLE) beacons. In such systems, the two most common techniques are trilateration and fingerprinting, with the latter providing higher accuracy. In the fingerprinting technique, Deep Learning (DL) algorithms are often used to predict the location of the receiver based on the RSSI measurements of multiple beacons received at the receiver. In this paper, we address two practical issues with applying Deep Learning to wireless localization -- transfer of solution from one wireless environment to another \emph{and} small size of labelled data set. First, we apply automatic hyperparameter optimization to a deep neural network (DNN) system for indoor wireless localization, which makes the system easy to port to new wireless environments. Second, we show how to augment a typically small labelled data set using the unlabelled data set. We observed improved performance in DL by applying the two techniques. Additionally, all relevant code has been made freely available.