Enhancing UWB Indoor Positioning using Bias-Aware EKF and Anchor Self-Localization

Abstract. Ultra-Wideband (UWB) technology is gaining attention for indoor positioning due to its high accuracy, low latency and resilience to interference, making it ideal for environments where GNSS (Global Navigation Satellite System) signals are unavailable—such as warehouses, hospitals, and underground facilities. However, UWB systems can suffer from reduced accuracy under Non-Line-of- Sight (NLOS) conditions and dynamic deployments. This paper proposes a novel bias aware EKF (Extend Kalman Filter) model, combined with Anchor Self-Localization method for localization in indoor environments, and enhancing the flexible deployment of anchors. The proposed model demonstrates an overall improvement of 32% and 41% in positioning accuracy compared to traditional methods across both indoor and outdoor environments respectively. The paper demonstrates that the proposed ASL method performs at par with conventional pre-calibrated methods where anchors are to be localized manually. Together, the Bias- Aware filtering and ASL approach enhance the scalability and reliability of UWB-based Indoor Positioning Systems (IPS) for real-world applications.