Unsupervised radar signal recognition based on multi‐block – Multi‐view Low‐Rank Sparse Subspace Clustering
In the field of radar reconnaissance, unsupervised recognition of radar signals is a particularly important method for classifying different signals and estimating signal parameters. For untagged signals, a new unsupervised recognition method based on the time‐frequency (TF) analysis and Multi‐view...
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| Published in | IET radar, sonar & navigation Vol. 16; no. 3; pp. 542 - 551 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Wiley
01.03.2022
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1751-8784 1751-8792 1751-8792 |
| DOI | 10.1049/rsn2.12201 |
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| Abstract | In the field of radar reconnaissance, unsupervised recognition of radar signals is a particularly important method for classifying different signals and estimating signal parameters. For untagged signals, a new unsupervised recognition method based on the time‐frequency (TF) analysis and Multi‐view Low‐Rank Sparse Subspace Clustering (MLRSSC) is proposed. Specifically, the authors use image wavelet decomposition to create four views for TF images (TFIs), which introduces the concept of multi‐view subspace clustering into radar signal recognition. Compared with other advanced clustering algorithms, the authors' algorithm pays more attention to combining the inherent properties of TFIs. Aiming at the unique ridgeline distribution phenomenon of TFI, a multi‐block technology capable of robustly distributing TFIs subspaces is proposed. Then, Multi‐block and MLRSSC are combined to propose the Multi‐block Joint Multi‐view Subspace Clustering (M‐MLRSSC). The simulation shows that in the TFIs data set with a certain signal‐to‐noise ratio, M‐MLRSSC has obtained superior results, which is better than a variety of advanced subspace clustering algorithms. |
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| AbstractList | In the field of radar reconnaissance, unsupervised recognition of radar signals is a particularly important method for classifying different signals and estimating signal parameters. For untagged signals, a new unsupervised recognition method based on the time‐frequency (TF) analysis and Multi‐view Low‐Rank Sparse Subspace Clustering (MLRSSC) is proposed. Specifically, the authors use image wavelet decomposition to create four views for TF images (TFIs), which introduces the concept of multi‐view subspace clustering into radar signal recognition. Compared with other advanced clustering algorithms, the authors' algorithm pays more attention to combining the inherent properties of TFIs. Aiming at the unique ridgeline distribution phenomenon of TFI, a multi‐block technology capable of robustly distributing TFIs subspaces is proposed. Then, Multi‐block and MLRSSC are combined to propose the Multi‐block Joint Multi‐view Subspace Clustering (M‐MLRSSC). The simulation shows that in the TFIs data set with a certain signal‐to‐noise ratio, M‐MLRSSC has obtained superior results, which is better than a variety of advanced subspace clustering algorithms. Abstract In the field of radar reconnaissance, unsupervised recognition of radar signals is a particularly important method for classifying different signals and estimating signal parameters. For untagged signals, a new unsupervised recognition method based on the time‐frequency (TF) analysis and Multi‐view Low‐Rank Sparse Subspace Clustering (MLRSSC) is proposed. Specifically, the authors use image wavelet decomposition to create four views for TF images (TFIs), which introduces the concept of multi‐view subspace clustering into radar signal recognition. Compared with other advanced clustering algorithms, the authors' algorithm pays more attention to combining the inherent properties of TFIs. Aiming at the unique ridgeline distribution phenomenon of TFI, a multi‐block technology capable of robustly distributing TFIs subspaces is proposed. Then, Multi‐block and MLRSSC are combined to propose the Multi‐block Joint Multi‐view Subspace Clustering (M‐MLRSSC). The simulation shows that in the TFIs data set with a certain signal‐to‐noise ratio, M‐MLRSSC has obtained superior results, which is better than a variety of advanced subspace clustering algorithms. |
| Author | Xu, Shuai Liu, Lutao |
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| Cites_doi | 10.1007/s00521-019-04494-1 10.1109/TPAMI.2013.57 10.1016/j.sigpro.2003.10.019 10.1016/j.patrec.2013.08.006 10.1109/TPAMI.2012.57 10.1038/nature14539 10.1088/1742‐6596/1634/1/012116 10.1109/TCSET.2018.8336160 10.1109/ACCESS.2020.2980363 10.1109/ACCESS.2017.2716191 10.1109/TGRS.2016.2524557 10.1049/iet‐rsn.2019.0436 10.1016/0165-1684(94)00150-X 10.1561/2200000016 10.1007/978-3-642-33786-4_26 10.1016/j.knosys.2019.105102 10.1109/34.868688 10.1109/CVPR.2014.134 10.1016/j.inffus.2010.03.002 10.1109/TIP.2018.2848470 10.1016/j.patcog.2019.107175 10.1109/WACV.2014.6836065 10.1109/TNNLS.2019.2958324 10.1016/j.neucom.2019.10.074 10.1007/BF01908075 10.1145/1390156.1390294 10.1049/iet-rsn.2013.0088 10.1016/j.patcog.2017.08.024 10.1109/MSP.2010.939739 10.3390/sym9050075 10.1109/ICSAI.2014.7009379 10.1109/TPAMI.2012.88 10.1016/j.patcog.2007.05.018 10.3390/electronics8040463 10.1109/CVPR.2011.5995365 |
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| Title | Unsupervised radar signal recognition based on multi‐block – Multi‐view Low‐Rank Sparse Subspace Clustering |
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