SHSLoc: Semi-Hard Sample Calibration and BCLPSO for Indoor Localization

Indoor localization is critical for applications in internet of thing, artificial intelligence, and location-based services. However, existing methods lack dynamic optimization mechanisms to address ambiguous boundary sample, resulting in insufficient fingerprint discrimination, high confusion, and limited accuracy in similar environments. Therefore, this paper proposes an indoor localization method based on semi-hard sample calibration and bayesian comprehensive learning particle swarm optimization (BCLPSO). This method includes offline training and online localization. In the offline training, channel state information (CSI) undergoes phase extraction and preprocessing to enhance angle of arrival (AoA) estimation accuracy. CSI samples are constructed using AoA and processed through a designed LocNet network. Then, the directional mining strategy is introduced to screen semi-hard samples, and the training triplet is dynamically constructed accordingly. Finally, LocNet is embedded as a weight-shared sub-network within the triplet architecture, and an adaptive discriminant boundary loss function is designed for network calibration. High-discriminability feature vectors extracted by the calibrated network are used to build the fingerprint database. In the online localization, features from target data are mapped into the embedding space using the calibrated triplet network. Position estimation is performed via BCLPSO. Extensive experiments in meeting room and laboratory environments show that the MAE and STD of the SHSLoc are 1.59 and 1.02 m (meeting room) and 2.02 and 1.50 m (laboratory), respectively, which are superior to the baseline method in terms of positioning accuracy and stability.