Abstract
Accurate indoor localization remains difficult in GPS-denied environments because Wi-Fi received signal strength indicator (RSSI) fingerprints are sparse, noisy, device-dependent, and strongly affected by multipath propagation, non-line-of-sight conditions, and environmental dynamics. To address these challenges, we present IndoorCART (Indoor Context-Aware RSSI Transformer), an encoder-only transformer framework designed specifically for coordinate-regression-based Wi-Fi fingerprinting. IndoorCART represents access-point measurements as RSSI tokens, incorporates learnable access-point identity information, applies missing-signal-aware Adaptive Attention to model contextual dependencies among observed and unobserved signals, and combines CLS-token and global-average representations for robust coordinate prediction. The model is evaluated on three public indoor-localization benchmarks, namely, UJIIndoorLoc, TUJI1, and SODIndoorLoc, using a controlled comparison with nine reproducible regression baselines. IndoorCART achieves mean absolute errors of 4.79 m, 0.18 m, and 1.33 m on UJIIndoorLoc, TUJI1, and SODIndoorLoc, respectively, with corresponding
R
2
scores of 0.99, 0.98, and 0.99. These results show that the proposed context-aware transformer consistently provides the lowest localization error among the implemented baselines and is particularly beneficial in sparse or structurally complex fingerprinting scenarios. We further position the proposed method against recent transformer-based indoor-localization studies through a literature-level comparison, while explicitly distinguishing reported external results from controlled reimplementations. Overall, IndoorCART offers an accurate and reliable framework for Wi-Fi-based indoor positioning; however, practical deployment should further consider model size, inference latency, energy consumption, device heterogeneity, and the modest performance gain observed on densely sampled datasets such as UJIIndoorLoc.