A Novel Self-Calibrated UWB-Based Indoor Localization Systems for Context-Aware Applications

Location information is the most crucial information used in context-aware applications, e-commerce and IoT-based consumer applications. Traditional methods doesn't focus on network coverage, accuracy, hardware cost, and noise in dense environment. To defeat these issues, this paper presents a novel localization algorithm for UWB nodes adopting self-calibration and ToA measurement for context-aware applications. The Link quality induction values are used instead of RSSI for distance estimation by costing technique. A calibration factor (CF) is further introduce to automatically update the location information in mobility. As the signal strength can be distorted heavily due to shadowing and multi-path fading, the localization is estimated in noisy condition and extended Kalman filtering (EKF) is applied to refine the node coordinates. Simulation results shows that the positioning error is decreased with an overall accuracy of 0.23m and standard-deviation of 0.76m.