User data selection method using received signal strength indicator for semi-supervised learning in fingerprint localization

Abstract

The fingerprint technique is used as an indoor localization method. This study uses a CNN-based indoor fingerprint localization method. The estimation accuracy of CNN improves as the number of AP information (AP identifiers and received signal strength indicator) increases. However, gathering AP information is time-consuming and costly. The problem can be solved using UD (AP information users measured). However, the UD measuring method does not know the user’s exact location. Therefore, it is essential to choose UD that is accurately estimate and use it for CNN training. In this study, we propose a method for selecting UDs that makes use of the RSSI similarity between AP information and UD.