dc.contributor.author | Waqar, Sahil | |
dc.contributor.author | Muaaz, Muhammad | |
dc.contributor.author | Pätzold, Matthias Uwe | |
dc.date.accessioned | 2023-12-19T09:39:29Z | |
dc.date.available | 2023-12-19T09:39:29Z | |
dc.date.created | 2023-09-07T17:21:36Z | |
dc.date.issued | 2023 | |
dc.identifier.citation | Waqar, S., Muaaz, M. & Pätzold, M. U. (2023). Direction-Independent Human Activity Recognition Using a Distributed MIMO Radar System and Deep Learning. IEEE Sensors Journal, 23 (20), 24916-24929. | en_US |
dc.identifier.issn | 1558-1748 | |
dc.identifier.uri | https://hdl.handle.net/11250/3108147 | |
dc.description.abstract | Modern monostatic radar-based human activity recognition (HAR) systems perform very well as long as the direction of human activities is either towards or away from the radar. The monostatic single-input single-output (SISO) and monostatic multiple-input multiple-output (MIMO) radar systems cannot detect motion of an object that moves perpendicularly to the radar’s boresight axis. Due to this physical layer limitation, today’s radar-based HAR systems fail to classify multi-directional human activities. In this paper, we resolve this typical but critical physical layer problem of contemporary HAR systems. We propose a HAR system underlying a distributed MIMO radar configuration, where multiple antennas of a millimeter wave MIMO radar system (Ancortek SDR-KIT 2400T2R4) are distributed in an indoor environment. In our proposed HAR system, we have two independent and identical monostatic radar subsystems that irradiate and capture the multi-directional human movement from two perspectives, which allows to compute two distinct time-variant radial velocity distributions. A feature extraction network extracts numerous features from the measured time-variant radial velocity distributions, which are then fused by a multiclass classifier to detect five types of human activities. The proposed multi-perspective MIMO-radar-based HAR system achieves a classification accuracy of 98.52%, which surpasses the accuracy of SISO radar-based HAR system by more than 9%. Our approach resolves the physical layer limitations of modern HAR systems that are based on either monostatic SISO or monostatic MIMO radar systems. | en_US |
dc.description.abstract | Direction-Independent Human Activity Recognition Using a Distributed MIMO Radar System and Deep Learning | en_US |
dc.language.iso | eng | en_US |
dc.publisher | IEEE | en_US |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.title | Direction-Independent Human Activity Recognition Using a Distributed MIMO Radar System and Deep Learning | en_US |
dc.title.alternative | Direction-Independent Human Activity Recognition Using a Distributed MIMO Radar System and Deep Learning | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | publishedVersion | en_US |
dc.rights.holder | © 2023 The Author(s) | en_US |
dc.subject.nsi | VDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550 | en_US |
dc.source.pagenumber | 24916-24929 | en_US |
dc.source.volume | 23 | en_US |
dc.source.journal | IEEE Sensors Journal | en_US |
dc.source.issue | 20 | en_US |
dc.identifier.doi | 10.1109/JSEN.2023.3310620 | |
dc.identifier.cristin | 2173323 | |
dc.relation.project | Universitetet i Agder: Wisenet | en_US |
dc.relation.project | Norges forskningsråd: 300638 | en_US |
cristin.qualitycode | 2 | |