Author(s): Klus, Lucie ; Klus, Roman ; Torres-Sospedra, Joaquín ; Lohan, Elena Simona ; Silva, Ivo Miguel Menezes ; Pendão, Cristiano Gonçalves ; Valkama, Mikko
Date: 2024
Persistent ID: https://hdl.handle.net/1822/96117
Origin: RepositóriUM - Universidade do Minho
Subject(s): Fingerprinting; Indoor Positioning; Intersection over Union; IoU; k-Nearest Neighbors; Localization
In modern wireless networks evolving towards 6th generation, localization, and sensing in indoor environments play an increasingly critical role in ensuring reliability, security, and control over network users, including vehicular assets. Despite recent advancements in deep learning, using k-Nearest Neighbors (k-NN) as a positioning algorithm in Received Signal Strength Indicator (RSSI) fingerprinting-based localization still provides numerous advantages, including localization accuracy, reliability, and interpretability. In this work, we introduce Intersection over Union (IoU) as a novel similarity metric and introduce κ-enhanced k-NN, which enables dynamic neighbor selection leading to improved performance and generalization capabilities of the positioning algorithm. In the evaluation using 26 publicly available indoor positioning datasets, we clearly show the improvements in localization accuracy of the combined IoU with κ-enhanced k-NN over the relevant baselines.
