Smartphone UWB Localization via Inconsistency-Aware Robust Trilateration

Abstract

Smartphone-based ultrawideband (UWB) localization is sensitive to anchor orientation, smartphone posture, and motion state, which cause condition-dependent ranging errors. We propose Inconsistency-Aware Robust Trilateration (IART), a lightweight framework that predicts per-anchor inconsistency and uses it to adapt measurement uncertainty within an Iteratively Reweighted Least Squares Gauss–Newton (IRLS-GN) solver. Combined with residual-based robust reweighting, IART suppresses unreliable range measurements without temporal filtering or IMU fusion. Experiments were conducted in a furnished indoor office using three anchors and two UWB-enabled iPhones. The evaluation covered 18 combinations of three anchor-orientation configurations (each fixed during an experimental run), three smartphone postures, and two motion states. Data were collected along two trajectories within the same environment and fixed anchor layout: Trajectory 1 for model development and same-trajectory evaluation, and Trajectory 2 for evaluating robustness to trajectory change. On Trajectory 2, IART reduced the mean RMSE from 0.350 m to 0.329 m (a 6.0% improvement) and achieved the lowest mean RMSE among all evaluated methods. It performed worse than Raw in only 2 of the 18 conditions, compared with 7 conditions for explicit signed-bias correction. Although signed-bias correction achieved higher average condition-wise gains on both trajectories, its gain decreased sharply on Trajectory 2 and it showed more frequent condition-wise degradation. These results support the same-deployment cross-trajectory robustness of learned inconsistency-aware weighting in three-anchor smartphone UWB localization. The current implementation assumes externally available smartphone-posture and motion-state labels.