EA-CGAN: Enhanced Attention Conditional GANs for Few-Sample Indoor Localization
Abstract
With the rapid advancement of smart cities, the demand for accurate indoor positioning is increasing. Traditional methods face challenges from high data collection costs and multipath effects. This letter proposes an Enhanced Attention Conditional GANs (EA-CGAN) based on fingerprinting for few-sample scenarios. By integrating SE and CBAM attention mechanisms, it optimizes network architecture and enhances spatial and channel feature extraction. EA-CGAN generates high-quality augmented RSS fingerprint databases from CGAN class labels, even with limited data. Experiments show that with only 20% real data, it achieves localization accuracy comparable to conventional methods, significantly reducing costs.
Fields of Study & Subjects
Indoor and Outdoor Localization Technologies
Optical Wireless Communication Technologies
Human Mobility and Location-Based Analysis
Publication details
- Journal / source
- IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences
- Volume
- E109.A
- Issue
- 8
- Pages
- 1414–1418
- Publication date
- 15 February 2026
- Language
- EN