Exploiting 2-D Representations for Enhanced Indoor Localization: A Transfer Learning Approach

This is the authors' abstract. We don't add key points for this paper.

Indoor localization systems predominantly depend on one-dimensional signal measurements, such as the Received Signal Strength Indication (RSSI) from Bluetooth or WiFi access points (AP). Such methods, however, grapple with issues like interference from other APs and environmental challenges. To address these, our paper introduces an innovative indoor localization technique employing a classification system bolstered by transfer learning. Instead of relying solely on one-dimensional signals, we transform them into images using techniques like spectrograms, scalograms, or Gramian Angular Fields. These transformed images feed into our classification system using a transfer learning approach. We tested our method on two public datasets, achieving remarkable accuracy rates of 99% with the Google-Net model and 98% with the Squeeze-Net architecture. These figures underscore the efficacy of our technique for indoor localization, marking a notable advancement over existing strategies.