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SangHyun Chang

dblp:21/8370 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2010
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-authorSystems, architecture and hardware · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer networks
1 paper
Wireless sensing and localization · 93% Physical-layer communications · 7%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Wireless sensing and localization › tracking
human detection and tracking
0.112010
Human detection and tracking via Ultra-Wideband (UWB) radar · ICRA 2010
Wireless sensing and localization › tracking
multi-target tracking
0.112010
Human detection and tracking via Ultra-Wideband (UWB) radar · ICRA 2010
Wireless sensing and localization
radar sensing
0.112010
Human detection and tracking via Ultra-Wideband (UWB) radar · ICRA 2010
Wireless sensing and localization › radar sensing
ultra-wideband radar
0.112010
Human detection and tracking via Ultra-Wideband (UWB) radar · ICRA 2010
Physical-layer communications › signal detection › hypothesis testing
likelihood ratio detection
0.012010
Human detection and tracking via Ultra-Wideband (UWB) radar · ICRA 2010

Methods — techniques the papers use, named apart from their topics

specular multi-path scattering model · 0.1multiple-hypothesis tracking · 0.1likelihood ratio detector · 0.1
YearPublicationVenuePosition
2010 Human detection and tracking via Ultra-Wideband (UWB) radar
abstract
This paper presents an algorithm for human presence detection and tracking using an Ultra-Wideband (UWB) impulse-based mono-static radar. UWB radar can complement other human tracking technologies, as it works well in poor visibility conditions. UWB electromagnetic wave scattering from moving humans forms a complex returned signal structure which can be approximated to a specular multi-path scattering model (SMPM). The key technical challenge is to simultaneously track multiple humans (and non-humans) using the complex scattered waveform observations. We develop a multiple-hypothesis tracking (MHT) framework that solves the complicated data association and tracking problem for an SMPM of moving objects/targets. Human presence detection utilizes SMPM signal features, which are tested in a classical likelihood ratio (LR) detector framework. The process of human detection and tracking is a combination of the MHT method and the LR human detector. We present experimental results in which a mono-static UWB radar tracks human and non-human targets, and detects human presence by discerning human from moving non-human objects.
SangHyun Chang, Michael T. Wolf, Joel W. Burdick
ICRA1