  Wireless fingerprint-based localization has become one of the most promising
technologies for ubiquitous location-aware computing and intelligent
location-based services. However, due to RF vulnerability to environmental
dynamics over time, continuous radio map updates are time-consuming and
infeasible, resulting in severe accuracy degradation. To address this issue, we
propose a novel approach of robust localization with dynamic adversarial
learning, known as DadLoc which realizes automatic radio map adaptation by
incorporating multiple robust factors underlying RF fingerprints to learn the
evolving feature representation with the complicated environmental dynamics.
DadLoc performs a finer-grained distribution adaptation with the developed
dynamic adversarial adaptation network and quantifies the contributions of both
global and local distribution adaptation in a dynamics-adaptive manner.
Furthermore, we adopt the strategy of prediction uncertainty suppression to
conduct source-supervised training, target-unsupervised training, and
source-target dynamic adversarial adaptation which can trade off the
environment adaptability and the location discriminability of the learned deep
representation for safe and effective feature transfer across different
environments. With extensive experimental results, the satisfactory accuracy
over other comparative schemes demonstrates that the proposed DanLoc can
facilitate fingerprint-based localization for wide deployments.
