Article (Périodiques scientifiques)
Occlusion-Informed Radar Detection for Millimeter-Wave Indoor Sensing
MURTADA, Ahmed Abdelnaser Elsayed; Rao, Bhavani Shankar Mysore Rama; AHMADI, Moein et al.
2024In IEEE open journal of signal processing, 5, p. 976 - 990
Peer reviewed vérifié par ORBi
 

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Mots-clés :
detection; hypothesis testing; indoor sensing; Millimeter-wave radar; MIMO sensors; occlusion-informed; Detection; Hypothesis testing; Indoor sensing; Millimetre-wave radar; Multiple inputs; Multiple outputs; Multiple-input multiple-output sensor; Occlusion-informed; Radar detection; Sensor phenomenon and characterizations; Signal Processing; Sensors; Radar; Radar imaging; Detectors; Sensor phenomena and characterization; Testing
Résumé :
[en] The emergence of Multiple-Input Multiple-Output (MIMO) millimeter-wave (mmWave) radar sensors has prompted interest in indoor sensing applications, including human detection, vital signs monitoring, and real-time tracking in crowded environments. These sensors, equipped with multiple antenna elements, offer high angular resolution, often referred to as imaging radars for their capability to detect high-resolution point clouds. Employing radar systems with high-angular resolution in occlusion-prone scenarios often results in sparse signal returns in range profiles. In extreme cases, only one target return may be observed, as the resolution grid size becomes significantly smaller than the targets, causing portions of the targets to consistently occupy the full area of a test cell. Leveraging this structure, we propose two detectors to enhance the detection of non-occluded targets in such scenarios, thereby providing accurate high-resolution point clouds. The first method employs multiple hypothesis testing over each range profile where the range cells within are considered mutually occluding. The second is formulated based on binary hypothesis testing for each cell, considering the distribution of the signal in the other cells within the same range profile. Numerical analysis demonstrates the superior performance of the latter method over both the classic detection and the former method, especially in low Signal-to-Noise Ratio (SNR) scenarios. Our work showcases the potential of occlusion-informed detection in imaging radars to improve the detection probability of non-occluded targets and reduce false alarms in challenging indoor environments.
Centre de recherche :
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > SPARC- Signal Processing Applications in Radar and Communications
Disciplines :
Ingénierie électrique & électronique
Auteur, co-auteur :
MURTADA, Ahmed Abdelnaser Elsayed  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SPARC
Rao, Bhavani Shankar Mysore Rama ;  University of Luxembourg, Interdisciplinary Centre for Security, Reliability and Trust (SnT), Luxembourg City, Luxembourg
AHMADI, Moein  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SPARC
Schroeder, Udo ;  IEE S.A., Bissen, Luxembourg
Co-auteurs externes :
no
Langue du document :
Anglais
Titre :
Occlusion-Informed Radar Detection for Millimeter-Wave Indoor Sensing
Date de publication/diffusion :
15 août 2024
Titre du périodique :
IEEE open journal of signal processing
eISSN :
2644-1322
Maison d'édition :
Institute of Electrical and Electronics Engineers Inc.
Volume/Tome :
5
Pagination :
976 - 990
Peer reviewed :
Peer reviewed vérifié par ORBi
Focus Area :
Computational Sciences
Projet FnR :
IF/15364040/RADII
Organisme subsidiant :
Luxembourg National Research Fund
N° du Fonds :
IF/15364040/RADII
Subventionnement (détails) :
This research was funded in whole, or in part, by the Luxembourg National Research Fund (FNR), grant reference [IF/15364040/RADII]. For the purpose of open access, and in fulfillment of the obligations arising from the grant agreement, the author has applied a Creative Commons Attribution 4.0 International (CC BY 4.0) license to any Author Accepted Manuscript version arising from this submission.
Disponible sur ORBilu :
depuis le 09 novembre 2024

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