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

• المؤلفون الرئيسيون: Yean, Seanglidet, Goh, Wayne, Lee, Bu-Sung, Oh, Hong Lye
• مؤلفون آخرون: School of Computer Science and Engineering
• التنسيق: مقال
• اللغة: English
• منشور في: 2023
• الموضوعات: Engineering::Computer science and engineering; Indoor Localisation; Generative Adversarial Networks
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/169535
• المؤسسة: 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...