Indoor Positioning Method Based on Multi-Objective Semantic Structure Inference of WiFi FTM

WiFi Fine Timing Measurement (FTM) directly provides round-trip delay ranging information between a mobile terminal and an access point (AP), serving as a critical technical foundation for the development of low-cost indoor positioning systems. However, indoor wall occlusion, multipath propagation, and intermittent link outages introduce spatially correlated systematic biases into FTM ranging measurements, rendering traditional multilateration methods vulnerable to abnormal observation outliers. To address this issue, this paper proposes an indoor positioning algorithm based on multi-objective semantic structure inference for WiFi FTM. Given available FTM ranging values, measurement masks, timestamps, and known AP coordinates, the proposed method first constructs FTM feature labels using AP distance vectors, distance differentials, and time differentials. Subsequently, wireless semantic embeddings are obtained by modeling masked AP distance measurements and implementing temporal-proximity contrastive learning. The implicit latent states derived from embedding clustering are interpreted as typical ranging pattern states, and state-dependent AP ranging biases are adopted to characterize non-line-of-sight (NLOS) errors under diverse spatial topographies. A semantic field and state transition matrix are further employed to model the spatial distribution and temporal adjacency relationships of these ranging states. Finally, by alternately optimizing a composite objective function consisting of Student-t mixture likelihood, FTM differential constraints, and link reliability constraints, the terminal position is estimated in a joint and robust manner. Experiments show that the mean error of this algorithm is 0.66 m, the median error is 0.64 m, and the 90th percentile error is 0.90 m. Compared with the conventional FTM least-squares multilateration baseline, the proposed method reduces the RMSE and mean error by 72.27% and 60.77%, respectively. Compared with the best conventional baseline (PSO), the corresponding reductions are 36.65% and 28.4%. These results indicate that visibility, relative distance, and residual variations within the FTM sequence can form useful implicit topological constraints for localization. These absolute accuracies are specific to the tested experimental setup. The relative improvements are reported to indicate the gain over conventional methods.