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".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Pesquisamos esquema de rastreamento multialvo sem dispositivo (DF) neste artigo. Os algoritmos de localização e rastreamento existentes sempre prestam atenção ao alvo único e precisam coletar uma grande quantidade de informações de localização. Neste artigo, exploramos a propriedade esparsa de múltiplos locais de destino para obter o rastreamento do alvo com precisão e com muito menos amostragem tanto nos links sem fio quanto nos intervalos de tempo. A abordagem proposta inclui principalmente a parte de localização do alvo e a parte de recuperação do rastreamento do alvo. Na parte de localização do alvo, explorando a dispersão inerente do número alvo, o Compression Sensing (CS) é utilizado para reduzir os links sem fio distribuídos. Na parte de recuperação do traço alvo, exploramos a propriedade compressiva do traço alvo, bem como projetamos a matriz de medição e a matriz esparsa, para reduzir as amostragens no domínio do tempo. Além disso, a teoria Kronecker Compression Sensing (KCS) é usada para recuperar simultaneamente os múltiplos traços do rótulo X e do rótulo Y. Finalmente, as simulações mostram que a abordagem proposta mantém um desempenho de recuperação eficaz.
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
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Copiar
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
Copiar
@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},}
Copiar
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 -