2019/09/10 by Andrea Conti, Santiago Mazuelas, Stefania Bartoletti +3 · 185 citations
Computer Science · Engineering · #Algorithm #Artificial intelligence #Computer science #Computer security #Computer vision #Data mining #Detector #Distributed Sensor Networks and Detection Algorithms #Engineering #Gaussian #Indoor and Outdoor Localization Technologies #Internet of Things #Line-of-sight #Machine learning #Metric (unit) #Range (aeronautics) #Robustness (evolution) #Target Tracking and Data Fusion in Sensor Networks #Telecommunications
paper · open access · doi:10.1109/jproc.2019.2905854
published in Proceedings of the IEEE 107(11), 2240-2264 (Institute of Electrical and Electronics Engineers)
openalex publication_date 2019/09/10 · openalex created_date 2019/09/19 · openalex updated_date 2026/08/01
Location awareness is vital for emerging Internet-of-Things applications and opens a new era for Localization-of-Things. This paper first reviews the classical localization techniques based on single-value metrics, such as range and angle estimates, and on fixed measurement models, such as Gaussian distributions with mean equal to the true value of the metric. Then, it presents a new localization approach based on soft information (SI) extracted from intra- and inter-node measurements, as well as from contextual data. In particular, efficient techniques for learning and fusing different kinds of SI are described. Case studies are presented for two scenarios in which sensing measurements are based on: 1) noisy features and non-line-of-sight detector outputs and 2) IEEE 802.15.4a standard. The results show that SI-based localization is highly efficient, can significantly outperform classical techniques, and provides robustness to harsh propagation conditions.