The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Nous recherchons un système de suivi multicible sans appareil (DF) dans cet article. Les algorithmes de localisation et de suivi existants prêtent toujours attention à une cible unique et doivent collecter une grande quantité d’informations de localisation. Dans cet article, nous exploitons la propriété clairsemée de plusieurs emplacements cibles pour obtenir un suivi précis de la cible avec beaucoup moins d'échantillonnage à la fois dans les liaisons sans fil et dans les intervalles de temps. L’approche proposée comprend principalement la partie localisation de la cible et la partie récupération de la trace cible. Dans la partie localisation de la cible, en exploitant la rareté inhérente du nombre cible, la détection par compression (CS) est utilisée pour réduire les liaisons sans fil distribuées. Dans la partie récupération de la trace cible, nous exploitons la propriété compressive de la trace cible, ainsi que la conception de la matrice de mesure et de la matrice clairsemée, pour réduire les échantillonnages dans le domaine temporel. De plus, la théorie Kronecker Compressive Sensing (KCS) est utilisée pour récupérer simultanément les multiples traces de l’étiquette X et de l’étiquette Y. Enfin, les simulations montrent que l’approche proposée présente des performances de récupération efficaces.
Sixing YANG
Army Engineering University of PLA
Yan GUO
Army Engineering University of PLA
Dongping YU
Army Engineering University of PLA
Peng QIAN
Army Engineering University of PLA
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Sixing YANG, Yan GUO, Dongping YU, Peng QIAN, "Device-Free Targets Tracking with Sparse Sampling: A Kronecker Compressive Sensing Approach" in IEICE TRANSACTIONS on Communications,
vol. E102-B, no. 10, pp. 1951-1959, October 2019, doi: 10.1587/transcom.2018DRP0010.
Abstract: We research device-free (DF) multi-target tracking scheme in this paper. The existing localization and tracking algorithms are always pay attention to the single target and need to collect a large amount of localization information. In this paper, we exploit the sparse property of multiple target locations to achieve target trace accurately with much less sampling both in the wireless links and the time slots. The proposed approach mainly includes the target localization part and target trace recovery part. In target localization part, by exploiting the inherent sparsity of the target number, Compressive Sensing (CS) is utilized to reduce the wireless links distributed. In the target trace recovery part, we exploit the compressive property of target trace, as well as designing the measurement matrix and the sparse matrix, to reduce the samplings in time domain. Additionally, Kronecker Compressive Sensing (KCS) theory is used to simultaneously recover the multiple traces both of the X label and the Y Label. Finally, simulations show that the proposed approach holds an effective recovery performance.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2018DRP0010/_p
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@ARTICLE{e102-b_10_1951,
author={Sixing YANG, Yan GUO, Dongping YU, Peng QIAN, },
journal={IEICE TRANSACTIONS on Communications},
title={Device-Free Targets Tracking with Sparse Sampling: A Kronecker Compressive Sensing Approach},
year={2019},
volume={E102-B},
number={10},
pages={1951-1959},
abstract={We research device-free (DF) multi-target tracking scheme in this paper. The existing localization and tracking algorithms are always pay attention to the single target and need to collect a large amount of localization information. In this paper, we exploit the sparse property of multiple target locations to achieve target trace accurately with much less sampling both in the wireless links and the time slots. The proposed approach mainly includes the target localization part and target trace recovery part. In target localization part, by exploiting the inherent sparsity of the target number, Compressive Sensing (CS) is utilized to reduce the wireless links distributed. In the target trace recovery part, we exploit the compressive property of target trace, as well as designing the measurement matrix and the sparse matrix, to reduce the samplings in time domain. Additionally, Kronecker Compressive Sensing (KCS) theory is used to simultaneously recover the multiple traces both of the X label and the Y Label. Finally, simulations show that the proposed approach holds an effective recovery performance.},
keywords={},
doi={10.1587/transcom.2018DRP0010},
ISSN={1745-1345},
month={October},}
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TY - JOUR
TI - Device-Free Targets Tracking with Sparse Sampling: A Kronecker Compressive Sensing Approach
T2 - IEICE TRANSACTIONS on Communications
SP - 1951
EP - 1959
AU - Sixing YANG
AU - Yan GUO
AU - Dongping YU
AU - Peng QIAN
PY - 2019
DO - 10.1587/transcom.2018DRP0010
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E102-B
IS - 10
JA - IEICE TRANSACTIONS on Communications
Y1 - October 2019
AB - We research device-free (DF) multi-target tracking scheme in this paper. The existing localization and tracking algorithms are always pay attention to the single target and need to collect a large amount of localization information. In this paper, we exploit the sparse property of multiple target locations to achieve target trace accurately with much less sampling both in the wireless links and the time slots. The proposed approach mainly includes the target localization part and target trace recovery part. In target localization part, by exploiting the inherent sparsity of the target number, Compressive Sensing (CS) is utilized to reduce the wireless links distributed. In the target trace recovery part, we exploit the compressive property of target trace, as well as designing the measurement matrix and the sparse matrix, to reduce the samplings in time domain. Additionally, Kronecker Compressive Sensing (KCS) theory is used to simultaneously recover the multiple traces both of the X label and the Y Label. Finally, simulations show that the proposed approach holds an effective recovery performance.
ER -