A Generalization Method for Indoor Localization via Domain-Invariant Feature Learning

Deep learning methods have been widely applied in indoor localization to mitigate the impact of non-line-of-sight (NLOS) and multipath effects, achieving high-precision results. However, these methods have to integrate large amounts of data for training and often struggle to adapt to new and unseen scenarios. This letter proposes a domain generalization method based on domain adversarial training (DAT) and mutual information (MI) estimation, where cross-environment generalization localization is achieved by extracting domain-invariant features, eliminating the need for training data from new scenes. Experimental results demonstrate that the proposed method outperforms other deep learning approaches in terms of localization accuracy across various indoor environments.