Clustered Multi-Task Learning for Automatic Radar Target Recognition
Model training is a key technique for radar target recognition. Traditional model training algorithms in the framework of single task leaning ignore the relationships among multiple tasks, which degrades the recognition performance. In this paper, we propose a clustered multi-task learning, which ca...
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| Vydané v: | Sensors (Basel, Switzerland) Ročník 17; číslo 10; s. 2218 |
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| Abstract | Model training is a key technique for radar target recognition. Traditional model training algorithms in the framework of single task leaning ignore the relationships among multiple tasks, which degrades the recognition performance. In this paper, we propose a clustered multi-task learning, which can reveal and share the multi-task relationships for radar target recognition. To further make full use of these relationships, the latent multi-task relationships in the projection space are taken into consideration. Specifically, a constraint term in the projection space is proposed, the main idea of which is that multiple tasks within a close cluster should be close to each other in the projection space. In the proposed method, the cluster structures and multi-task relationships can be autonomously learned and utilized in both of the original and projected space. In view of the nonlinear characteristics of radar targets, the proposed method is extended to a non-linear kernel version and the corresponding non-linear multi-task solving method is proposed. Comprehensive experimental studies on simulated high-resolution range profile dataset and MSTAR SAR public database verify the superiority of the proposed method to some related algorithms. |
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| AbstractList | Model training is a key technique for radar target recognition. Traditional model training algorithms in the framework of single task leaning ignore the relationships among multiple tasks, which degrades the recognition performance. In this paper, we propose a clustered multi-task learning, which can reveal and share the multi-task relationships for radar target recognition. To further make full use of these relationships, the latent multi-task relationships in the projection space are taken into consideration. Specifically, a constraint term in the projection space is proposed, the main idea of which is that multiple tasks within a close cluster should be close to each other in the projection space. In the proposed method, the cluster structures and multi-task relationships can be autonomously learned and utilized in both of the original and projected space. In view of the nonlinear characteristics of radar targets, the proposed method is extended to a non-linear kernel version and the corresponding non-linear multi-task solving method is proposed. Comprehensive experimental studies on simulated high-resolution range profile dataset and MSTAR SAR public database verify the superiority of the proposed method to some related algorithms. Model training is a key technique for radar target recognition. Traditional model training algorithms in the framework of single task leaning ignore the relationships among multiple tasks, which degrades the recognition performance. In this paper, we propose a clustered multi-task learning, which can reveal and share the multi-task relationships for radar target recognition. To further make full use of these relationships, the latent multi-task relationships in the projection space are taken into consideration. Specifically, a constraint term in the projection space is proposed, the main idea of which is that multiple tasks within a close cluster should be close to each other in the projection space. In the proposed method, the cluster structures and multi-task relationships can be autonomously learned and utilized in both of the original and projected space. In view of the nonlinear characteristics of radar targets, the proposed method is extended to a non-linear kernel version and the corresponding non-linear multi-task solving method is proposed. Comprehensive experimental studies on simulated high-resolution range profile dataset and MSTAR SAR public database verify the superiority of the proposed method to some related algorithms.Model training is a key technique for radar target recognition. Traditional model training algorithms in the framework of single task leaning ignore the relationships among multiple tasks, which degrades the recognition performance. In this paper, we propose a clustered multi-task learning, which can reveal and share the multi-task relationships for radar target recognition. To further make full use of these relationships, the latent multi-task relationships in the projection space are taken into consideration. Specifically, a constraint term in the projection space is proposed, the main idea of which is that multiple tasks within a close cluster should be close to each other in the projection space. In the proposed method, the cluster structures and multi-task relationships can be autonomously learned and utilized in both of the original and projected space. In view of the nonlinear characteristics of radar targets, the proposed method is extended to a non-linear kernel version and the corresponding non-linear multi-task solving method is proposed. Comprehensive experimental studies on simulated high-resolution range profile dataset and MSTAR SAR public database verify the superiority of the proposed method to some related algorithms. |
| Author | Li, Cong Zhang, Hua Xu, Luping Bao, Weimin |
| AuthorAffiliation | School of Aerospace Science and Technology, Xidian University, Xi’an 710126, China; lcongxd@126.com (C.L.); baoweimin@cashq.ac.cn (W.B.); zhanghua@mail.xidian.edu.cn (H.Z.) |
| AuthorAffiliation_xml | – name: School of Aerospace Science and Technology, Xidian University, Xi’an 710126, China; lcongxd@126.com (C.L.); baoweimin@cashq.ac.cn (W.B.); zhanghua@mail.xidian.edu.cn (H.Z.) |
| Author_xml | – sequence: 1 givenname: Cong surname: Li fullname: Li, Cong – sequence: 2 givenname: Weimin surname: Bao fullname: Bao, Weimin – sequence: 3 givenname: Luping surname: Xu fullname: Xu, Luping – sequence: 4 givenname: Hua surname: Zhang fullname: Zhang, Hua |
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| Cites_doi | 10.3390/rs8080683 10.1007/s11222-008-9111-x 10.1109/TAES.2007.357120 10.1109/LGRS.2016.2608578 10.1016/j.neucom.2016.02.059 10.3390/app6010026 10.1109/LGRS.2015.2506659 10.1049/el.2016.3060 10.1145/2538028 10.1016/j.sigpro.2015.12.006 10.1109/JSTARS.2015.2436694 10.1016/j.neucom.2015.06.079 10.1145/1014052.1014067 10.1109/7.937475 10.1016/j.neucom.2015.08.111 10.1109/ICDM.2009.128 10.1109/TPAMI.2015.2452911 10.3390/s17010192 10.1016/j.neucom.2015.06.108 |
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| Keywords | synthetic aperture radar (SAR) radar automatic target recognition (RATR) high-resolution range profile (HRRP) clustered multi-task learning |
| Language | English |
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| Title | Clustered Multi-Task Learning for Automatic Radar Target Recognition |
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