Human Being Detection from UWB NLOS Signals: Accuracy and Generality of Advanced Machine Learning Models

Autor: Gianluca Moro, Federico Di Luca, Davide Dardari, Giacomo Frisoni
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Sensors, Vol 22, Iss 4, p 1656 (2022)
Druh dokumentu: article
ISSN: 22041656
1424-8220
DOI: 10.3390/s22041656
Popis: This paper studies the problem of detecting human beings in non-line-of-sight (NLOS) conditions using an ultra-wideband radar. We perform an extensive measurement campaign in realistic environments, considering different body orientations, the obstacles’ materials, and radar–obstacle distances. We examine two main scenarios according to the radar position: (i) placed on top of a mobile cart; (ii) handheld at different heights. We empirically analyze and compare several input representations and machine learning (ML) methods—supervised and unsupervised, symbolic and non-symbolic—according to both their accuracy in detecting NLOS human beings and their adaptability to unseen cases. Our study proves the effectiveness and flexibility of modern ML techniques, avoiding environment-specific configurations and benefiting from knowledge transference. Unlike traditional TLC approaches, ML allows for generalization, overcoming limits due to unknown or only partially known observation models and insufficient labeled data, which usually occur in emergencies or in the presence of time/cost constraints.
Databáze: Directory of Open Access Journals
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