Disentanglement Model for HRRP Target Recognition when Missing Aspects

Yanhua Wang, Yunchi Ma, Liang Zhang*, Junfu Wang, Yi Zhang, Hongfen Lv

*此作品的通讯作者

科研成果: 书/报告/会议事项章节会议稿件同行评审

1 引用 (Scopus)

摘要

High resolution range profile (HRRP) is an important approach in radar automatic target recognition (RATR). Numerous deep learning methods have been proposed for HRRP recognition. However, HRRP is sensitive to aspects. When the training dataset misses aspects, the performance of the deep learning models is limited. Motivated by disentangled representation learning (DRL), this paper proposes a type-aspect disentanglement model to solve the above aspect-missing problem. The proposed method aims to obtain uncorrelated type feature and aspect feature of HRRP via DRL, and uses the type feature for recognition. Specifically, the type and aspect features are explicitly obtained via a VGG11-based network. An adversarial decorrelation loss and a reconstruction loss are applied to guide the disentanglement process. Moreover, the true type and aspect labels are employed to supervise the two features' semantic discriminability. Experimental results demonstrate the proposed method effectively improves the recognition performance in aspect-missing cases.

源语言英语
主期刊名IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings
出版商Institute of Electrical and Electronics Engineers Inc.
5770-5773
页数4
ISBN(电子版)9798350320107
DOI
出版状态已出版 - 2023
活动2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023 - Pasadena, 美国
期限: 16 7月 202321 7月 2023

出版系列

姓名International Geoscience and Remote Sensing Symposium (IGARSS)
2023-July

会议

会议2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023
国家/地区美国
Pasadena
时期16/07/2321/07/23

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