A Research on Positioning Algorithm Based on RPCA in Sparse Fingerprint Environment - Yaqin Xie; Md Emadur Rahman Ekra; Tianyuan Gu; Xiaoli Wang (Universitas Komputer Indonesia, 2025)
Wi-Fi fingerprinting for indoor positioning is cost-effective but struggles with environmental noise and requires extensive data collection for high accuracy. To address these challenges in sparse fingerprint environments, this paper proposes a positioning technique using a Robust Principal Component Analysis (RPCA) algorithm. First, gathered signals are purified using measurement weights to mitigate outlier noise, saving the refined fingerprints in a database. Second, to reduce collection costs, virtual fingerprints are generated near reference points using a transmission loss model and stored offline. Finally, adaptive K-value fingerprint matching is applied to estimate the user's location. Results demonstrate that the proposed RPCA-based algorithm significantly improves positioning accuracy in sparse indoor environments.