Outlier-Robust Indoor Localization using Ultra WideBand Technology

Indoor localization has gained traction with exponential growth in IoT devices and location-based services. Indoor positioning systems rely on different localization methods other than GPS as its unsuitable for a closed environment. Ultra-Wideband (UWB) is a short-range radio communication protocol that can provide highly accurate low latency spatial and directional data. In a real-time indoor localization, aspects like accuracy and latency require consideration as environmental interference can introduce an anomaly, resulting in less accurate position estimation. If ignored, these outliers can introduce significant anomalies that can impact the overall reliability and stability of the system. The outliers can result from faulty hardware, signal distortion, and environmental aspects. In a sensor-based positioning system, optimization using statistical methods and algorithms is required to filter outliers and improve accuracy. Outlier detection is addressed in different research papers, resulting in innovative algorithms and methods. ADAPT (Adaptive Trimming) is a general-purpose algorithm originally proposed for outliers in problems related to spatial perception. The algorithm statistically outperformed other advanced solutions in removing outliers. In indoor localization, the position estimate's accuracy depends on removing outliers from the positioning data acquired from the sensors. The principal aspect of the thesis is to study the applicability and effectiveness of this algorithm for indoor localization