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.
Highlights
Ultra-wideband (UWB) radars have found application in several fields over the last decade, such as the biomedical and industrial ones, thanks to technological progress.the UWB-radar transmits exploratory signals with a much larger spectrum than conventional radars, resulting in significant advantages in localization owing to its centimeter-level range resolution, multipath robustness, high penetration ability, low cost and energy consumption
We examine various machine learning (ML) and deep learning (DL) methods to find the best classifier that can distinguish between the presence and absence of a normally-breathing human being under NLOS radio propagation conditions by adequately processing the backscattered UWB signal
The results revealed that the through-wall UWB switched-antenna-array radar could distinguish between human subjects and mannequins
Summary
Ultra-wideband (UWB) radars have found application in several fields over the last decade, such as the biomedical and industrial ones, thanks to technological progress. The UWB-radar transmits exploratory signals with a much larger spectrum than conventional radars, resulting in significant advantages in localization owing to its centimeter-level range resolution, multipath robustness, high penetration ability, low cost and energy consumption. One of the most impressive characteristics of these systems is their ability to locate human beings by detecting small chest movements while breathing [1], even in non-line-of-sight (NLOS) conditions. This capacity can be helpful in many situations. This paper presents an extensive measurement campaign with different environments, obstruction materials, and body orientations using an UWB-radar
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