CR-AP: A Wi-Fi fingerprint localization method based on capsule networks

Balancing positioning accuracy and deployment cost in indoor environments remains challenging. RSS-based Wi-Fi fingerprinting has emerged as a popular, low-cost approach; however, RSS is highly variant due to signal attenuation and multipath effects, which substantially degrade localization accuracy. This paper proposes CR-AP (Capsule Routing on AP-Centric Heterogeneous Graphs), a novel method based on capsule network for RSS-based Wi-Fi fingerprinting. Offline, we extract three types of relations from the radio map-AP-AP cosine-similarity kNN, RP-RP co-occurrence, and RP-AP visibility-to construct a heterogeneous graph. Online, the measurement is mapped into a capsule space, followed by one round of message passing over the graph and multi-round conditional dynamic routing to produce AP soft selection and RP soft weights, which are then fed into a lightweight coordinate regressor. We further introduce a reconstruction loss to align the soft weights with the actual observation. On the SODIndoorLoc dataset, CR-AP achieves a mean error of 2.07 m and a P90 of 3.94 m. In AP-missing experiments, CR-AP maintains strong robustness when less than 50% of APs are missing. Cross-scene evaluation on CETC331 also demonstrates good generalization. Compared with multiple baselines and recent models, CR-AP consistently delivers superior performance across several metrics.