Space Target Recognition of Radar Cross-Section Sequence Based on Transformer

Meng Xie (), Weiwei Wu, Chenchen Fu and Sujunjie Sun
Additional contact information
Meng Xie: Department of Computer Science and Engineering, Southeast University, Nanjing 210018, China
Weiwei Wu: Department of Computer Science and Engineering, Southeast University, Nanjing 210018, China
Chenchen Fu: Department of Computer Science and Engineering, Southeast University, Nanjing 210018, China
Sujunjie Sun: Department of Computer Science and Engineering, Southeast University, Nanjing 210018, China

Mathematics, 2025, vol. 13, issue 4, 1-13

Abstract: As the strategic importance of space continues to grow, space target recognition technology is critical for advancing space surveillance systems, optimizing the use of space resources, and ensuring space security. In response to challenges in analyzing radar cross-section (RCS) sequences, we propose a deep learning-based method designed to enhance the robustness and accuracy of RCS sequence analysis for space target recognition. We introduce a novel period estimation method based on combination functions and analysis of variance (ANOVA), which effectively suppresses noise and captures periodic characteristics with greater accuracy. Building on this, we propose a Transformer-based approach for size estimation from RCS sequences, leveraging Transformers’ advanced sequence modeling to reduce common errors in traditional methods, further improving space target characterization. We then integrate period and size features into a unified feature set and introduce a cross-attention-based multi-feature interaction module to fuse physical and statistical features, learning dependencies between them to enhance target recognition accuracy. Experimental results demonstrate that our approach significantly improves both performance and stability of space target recognition, providing a solid foundation for further advancements in space surveillance technology.

Keywords: space target recognition; radar cross-section; period estimation; size estimation; comprehensive features (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/4/653/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/4/653/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:4:p:653-:d:1592610

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().