extendGAN+: transferable data augmentation framework using WGAN-GP for data-driven indoor localisation model

• Main Authors: Yean, Seanglidet, Goh, Wayne, Lee, Bu-Sung, Oh, Hong Lye
• Other Authors: School of Computer Science and Engineering
• Format: Article
• Language: English
• Published: 2023
• Subjects: Engineering::Computer science and engineering; Indoor Localisation; Generative Adversarial Networks
• Online Access: https://hdl.handle.net/10356/169535
• Institution: Nanyang Technological University

For indoor localisation, a challenge in data-driven localisation is to ensure sufficient data to train the prediction model to produce a good accuracy. However, for WiFi-based data collection, human effort is still required to capture a large amount of data as the representation Received Signal Stre...