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- Title
Radar High-Resolution Range Profile Rejection Based on Deep Multi-Modal Support Vector Data Description.
- Authors
Dong, Yue; Wang, Penghui; Fang, Ming; Guo, Yifan; Cao, Lili; Yan, Junkun; Liu, Hongwei
- Abstract
Radar Automatic Target Recognition (RATR) based on high-resolution range profile (HRRP) has received intensive attention in recent years. In practice, RATR usually needs not only to recognize in-library samples but also to reject out-of-library samples. However, most rejection methods lack a specific and accurate description of the underlying distribution of HRRP, which limits the effectiveness of the rejection task. Therefore, this paper proposes a novel rejection method for HRRP, named Deep Multi-modal Support Vector Data Description (DMMSVDD). On the one hand, it forms a more compact rejection boundary with the Gaussian mixture model in consideration of the high-dimensional and multi-modal structure of HRRP. On the other hand, it captures the global temporal information and channel-dependent information with a dual attention module to gain more discriminative structured features, which are optimized jointly with the rejection boundary. In addition, a semi-supervised extension is proposed to refine the boundary with available out-of-library samples. Experimental results based on measured data show that the proposed methods demonstrate significant improvement in the HRRP rejection performance.
- Subjects
VECTOR data; GAUSSIAN mixture models; AUTOMATIC target recognition; RADAR targets
- Publication
Remote Sensing, 2024, Vol 16, Issue 4, p649
- ISSN
2072-4292
- Publication type
Article
- DOI
10.3390/rs16040649