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
본 논문에서는 DF(Device-Free) 다중 대상 추적 기법을 연구합니다. 기존의 위치 파악 및 추적 알고리즘은 항상 단일 대상에 주의를 기울이고 대량의 위치 파악 정보를 수집해야 합니다. 본 논문에서는 무선 링크와 시간 슬롯 모두에서 훨씬 적은 샘플링으로 정확하게 목표 추적을 달성하기 위해 여러 목표 위치의 희박한 특성을 활용합니다. 제안된 접근 방식은 주로 표적 위치 파악 부분과 표적 추적 복구 부분을 포함한다. 표적 위치 파악 부분에서는 표적 번호의 고유한 희소성을 활용하여 분산되는 무선 링크를 줄이기 위해 CS(Compressive Sensing)를 활용합니다. 타겟 트레이스 복구 부분에서는 타겟 트레이스의 압축 특성을 활용하고 측정 행렬과 희소 행렬을 설계하여 시간 영역에서 샘플링을 줄입니다. 또한 KCS(Kronecker Compressive Sensing) 이론을 사용하여 X 라벨과 Y 라벨의 다중 추적을 동시에 복구합니다. 마지막으로, 시뮬레이션은 제안된 접근 방식이 효과적인 복구 성능을 가지고 있음을 보여줍니다.
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
부
@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},}
부
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 -