A Machine Learning Approach to Ranging Error Mitigation for UWB Localization

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A Theoretical Foundation of Network Localization and Navigation (2018, background): “Localizing mobile nodes with unknown positions (called agents) can be accomplished by utilizing two types of measurements, namely, inter-node and intra-node measurements, together with prior knowledge [54]–[58] Internode measurements refer to those between nodes in a network through radio-frequency ”
A Novel NLOS Mitigation Algorithm for UWB Localization in Harsh Indoor Environments (2019, methodology): “In [29] and [30], support vector machine (SVM) is applied to distinguish between LOS and NLOS and to mitigate the NLOS range error by using the trained classification and”
Indoor Tracking: Theory, Methods, and Technologies (2015, background): “Most of them rely on the extraction of received signal features that are mainly affected by NLOS propagation and take a decision based on some a priori statistical knowledge or learning approach [36], [37].”
Online Sparse Multi-Output Gaussian Process Regression and Learning (2019, background): “GP regression has found diverse applications in indoor localization [3]–[5], image processing and visual tracking [6], [7], change point detection [8] and sensor (robotic) network management [9], [10].”