Human Being Detection from UWB NLOS Signals: Accuracy and Generality of Advanced Machine Learning Models release_teetdgmkpbb73ogekww4gbylqq

by Gianluca Moro, Federico Di Di Luca, DAVIDE DARDARI, Giacomo Frisoni

Published in Sensors by MDPI AG.

2022   Volume 22, Issue 4, p1656

Abstract

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.
In application/xml+jats format

Archived Files and Locations

application/pdf  2.6 MB
file_dapagsx4t5bq3etjw3v4tr4hly
mdpi-res.com (publisher)
web.archive.org (webarchive)

Web Captures

https://www.mdpi.com/1424-8220/22/4/1656/htm
2022-06-14 23:45:22 | 65 resources
webcapture_epmw3untmbhdllatxqhx55lv4m
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article-journal
Stage   published
Date   2022-02-20
Language   en ?
DOI  10.3390/s22041656
PubMed  35214558
PMC  PMC8879265
Container Metadata
Open Access Publication
In DOAJ
In ISSN ROAD
In Keepers Registry
ISSN-L:  1424-8220
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 173e6500-9242-4972-8772-d7fa2aa69088
API URL: JSON