IEICE TRANSACTIONS on Electronics
Multi-Target Recognition Utilizing Micro-Doppler Signatures with Limited Supervision
Summary :
Previously, convolutional neural networks have made tremendous progress in target recognition based on micro-Doppler radar. However, these studies only considered the presence of one target at a time in the surveillance area. Simultaneous multi-targets recognition for surveillance radar remains a pretty challenging issue. To alleviate this issue, this letter develops a multi-instance multi-label (MIML) learning strategy, which can automatically locate the crucial input patterns that trigger the labels. Benefitting from its powerful target-label relation discovery ability, the proposed framework can be trained with limited supervision. We emphasize that only echoes from single targets are involved in training data, avoiding the preparation and annotation of multi-targets echo in the training stage. To verify the validity of the proposed method, we model two representative ground moving targets, i.e., person and wheeled vehicles, and carry out numerous comparative experiments. The result demonstrates that the developed framework can simultaneously recognize multiple targets and is also robust to variation of the signal-to-noise ratio (SNR), the initial position of targets, and the difference in scattering coefficient.
- Publication
- IEICE TRANSACTIONS on Electronics Vol.E106-C No.8 pp.454-457
- Publication Date
- 2023/08/01
- Publicized
- 2023/03/06
- Online ISSN
- 1745-1353
- DOI
- 10.1587/transele.2022ECS6011
- Type of Manuscript
- BRIEF PAPER
- Category
- Electronic Instrumentation and Control
Authors
Jingyi ZHANG
Nanjing Normal University
Kuiyu CHEN
Nanjing University of Science and Technology
Yue MA
Nanjing University of Science and Technology
Keyword
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Jingyi ZHANG, Kuiyu CHEN, Yue MA, "Multi-Target Recognition Utilizing Micro-Doppler Signatures with Limited Supervision" in IEICE TRANSACTIONS on Electronics,
vol. E106-C, no. 8, pp. 454-457, August 2023, doi: 10.1587/transele.2022ECS6011.
Abstract: Previously, convolutional neural networks have made tremendous progress in target recognition based on micro-Doppler radar. However, these studies only considered the presence of one target at a time in the surveillance area. Simultaneous multi-targets recognition for surveillance radar remains a pretty challenging issue. To alleviate this issue, this letter develops a multi-instance multi-label (MIML) learning strategy, which can automatically locate the crucial input patterns that trigger the labels. Benefitting from its powerful target-label relation discovery ability, the proposed framework can be trained with limited supervision. We emphasize that only echoes from single targets are involved in training data, avoiding the preparation and annotation of multi-targets echo in the training stage. To verify the validity of the proposed method, we model two representative ground moving targets, i.e., person and wheeled vehicles, and carry out numerous comparative experiments. The result demonstrates that the developed framework can simultaneously recognize multiple targets and is also robust to variation of the signal-to-noise ratio (SNR), the initial position of targets, and the difference in scattering coefficient.
URL: https://global.ieice.org/en_transactions/electronics/10.1587/transele.2022ECS6011/_p
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@ARTICLE{e106-c_8_454,
author={Jingyi ZHANG, Kuiyu CHEN, Yue MA, },
journal={IEICE TRANSACTIONS on Electronics},
title={Multi-Target Recognition Utilizing Micro-Doppler Signatures with Limited Supervision},
year={2023},
volume={E106-C},
number={8},
pages={454-457},
abstract={Previously, convolutional neural networks have made tremendous progress in target recognition based on micro-Doppler radar. However, these studies only considered the presence of one target at a time in the surveillance area. Simultaneous multi-targets recognition for surveillance radar remains a pretty challenging issue. To alleviate this issue, this letter develops a multi-instance multi-label (MIML) learning strategy, which can automatically locate the crucial input patterns that trigger the labels. Benefitting from its powerful target-label relation discovery ability, the proposed framework can be trained with limited supervision. We emphasize that only echoes from single targets are involved in training data, avoiding the preparation and annotation of multi-targets echo in the training stage. To verify the validity of the proposed method, we model two representative ground moving targets, i.e., person and wheeled vehicles, and carry out numerous comparative experiments. The result demonstrates that the developed framework can simultaneously recognize multiple targets and is also robust to variation of the signal-to-noise ratio (SNR), the initial position of targets, and the difference in scattering coefficient.},
keywords={},
doi={10.1587/transele.2022ECS6011},
ISSN={1745-1353},
month={August},}
Copy
TY - JOUR
TI - Multi-Target Recognition Utilizing Micro-Doppler Signatures with Limited Supervision
T2 - IEICE TRANSACTIONS on Electronics
SP - 454
EP - 457
AU - Jingyi ZHANG
AU - Kuiyu CHEN
AU - Yue MA
PY - 2023
DO - 10.1587/transele.2022ECS6011
JO - IEICE TRANSACTIONS on Electronics
SN - 1745-1353
VL - E106-C
IS - 8
JA - IEICE TRANSACTIONS on Electronics
Y1 - August 2023
AB - Previously, convolutional neural networks have made tremendous progress in target recognition based on micro-Doppler radar. However, these studies only considered the presence of one target at a time in the surveillance area. Simultaneous multi-targets recognition for surveillance radar remains a pretty challenging issue. To alleviate this issue, this letter develops a multi-instance multi-label (MIML) learning strategy, which can automatically locate the crucial input patterns that trigger the labels. Benefitting from its powerful target-label relation discovery ability, the proposed framework can be trained with limited supervision. We emphasize that only echoes from single targets are involved in training data, avoiding the preparation and annotation of multi-targets echo in the training stage. To verify the validity of the proposed method, we model two representative ground moving targets, i.e., person and wheeled vehicles, and carry out numerous comparative experiments. The result demonstrates that the developed framework can simultaneously recognize multiple targets and is also robust to variation of the signal-to-noise ratio (SNR), the initial position of targets, and the difference in scattering coefficient.
ER -
English
