An Improved Triplet-Based Channel Charting Algorithm for Positioning via Covariance Feature

Traditional geometry-based positioning methods (GPMs) suffer from low accuracy in non-line-of-sight (NLOS) environments, while fingerprint-based methods (FPMs) encounter high maintenance costs due to the need for continuous database updates. As an emerging unsupervised technique, channel charting can address these challenges by mapping channel state information (CSI) into a low-dimensional virtual space that represents pseudo-positions of user equipments (UEs). In this paper, a channel charting-based positioning algorithm that leverages a covariance-based channel feature and a corresponding dissimilarity metric is proposed to enhance robustness against noise and timing advance (TA) interference. Additionally, an improved Triplet neural network algorithm is introduced, which enables channel charting to directly and accurately predict real geographical positions. Through ray tracing simulations, the proposed algorithm is compared with state-of-the-art channel charting-based positioning methods, demonstrating its effectiveness and superiority.