Geometry-Aware Deep Subdomain Adaptation for Robust Wi-Fi CSI Indoor Localization

Indoor localization based on Wi-Fi Channel State Information (CSI) continues to gain prominence as a practical, infrastructure-free solution for position estimation in dynamic indoor environments. However, CSI fingerprints are highly sensitive to environmental variations such as furniture displacement, door and window states, device heterogeneity, and temporal drift, which introduce domain shifts that significantly degrade localization accuracy. Recent domain adaptation methods mitigate these discrepancies, yet most rely solely on aligning marginal or conditional distributions and neglect the geometric structure of feature manifolds, often resulting in class confusion and distorted local neighborhoods. This study proposes a Geometry-Aware Deep Joint Multi-Domain Adaptation Network (G-DJMDAN) that integrates a deep residual CSI encoder, multi-layer distribution alignment, and a novel graph-based geometry-preservation module to maintain spatial topology across domains. By jointly optimizing classification loss, MK-MMD, LMMD, and Laplacian regularization, the framework produces more stable, discriminative embeddings under diverse environmental changes. Comprehensive experiments conducted across multiple real-world domain-shift scenarios—including temporal variation, furniture layout changes, and mixed perturbations—show that G-DJMDAN reduces median localization error by up to 34.7% compared with DJMDAN and achieves statistically significant improvements over CNN, DANN, DAN, JAN, and DSAN baselines. Visualization using t-SNE further confirms that the proposed geometry module effectively preserves manifold structure and mitigates target-domain collapse. Overall, the results demonstrate that incorporating geometric constraints into domain adaptation provides a robust and scalable pathway for high-precision indoor localization in non-stationary environments.