Learning Domain-Invariant Model for WiFi-Based Indoor Localization

WiFi-based indoor localization has gained widespread attention due to the pervasive availability of WiFi Access Points (APs). While signal processing-based methods can achieve decimeter-level localization, their performance is constrained by the limited spatial resolution of WiFi systems, especially in complex environments with strong interference. By contrast, deep learning-based methods have achieved impressive performance even in complex environments, which however often fail to generalize to new environments. In this paper, we propose a novel framework to learn domain-invariant model for WiFi-based indoor localization, which maintains impressive performance across different environments. The key insight is to design a deep learning-based WiFi localization system through the perspective of signal processing. Specifically, we let the neural network estimate APs-centered polar coordinates to avoid fitting the coordinates of APs strongly correlated with the environment, enabling us to obtain the domain-invariant model. To unleash the potential of neural networks in regressing high-precision parameters, we design a beamforming layer to integrate the knowledge of signal processing. Furthermore, we propose a multi-task learning scheme to further improve localization accuracy. Extensive experiments on diverse datasets have demonstrated that the localization performance of our method outperforms state-of-the-art methods and demonstrates superiority under cross-domain conditions.