Sequential learning for fingerprint based indoor localization

In this paper, we introduce a novel approach for improving performance of fingerprinting based indoor localization. Our proposal is a two-step procedure in which severe variation in the received signal strength is minimized during the first step via convex optimization, and distance metric learning is then used to estimate a more accurate location. Numerical results show that our proposal outperforms existing techniques in terms of accuracy and reliability.