Earticle

초록

영어

This paper proposes a vision-language model for the joint recognition of Low Probability of Intercept (LPI) radar signals through time-frequency distribution (TFD)-text alignment. The proposed framework unifies waveform classification and signal parameter estimation by aligning TFD spectrograms with hierarchical textual prompts in a shared embedding space. To support both general waveform type recognition and fine-grained parameter inference, we introduce a prompt dropout strategy that balances rich and simple prompts during training. Evaluated on multiple TFD representations including SPWVD, CWD, and SAFI, the model demonstrates high accuracy and interpretability across both tasks. This unified approach offers a compact, extensible solution for LPI radar signal understanding.

목차

Abstract
I. INTRODUCTION
II. RELATED WORKS
III. METHOD
A. Contrastive Vision–Language Modeling on TFD
B. Hierarchical Text Prompt
C. Prompt Dropout Strategy
D. Validation Strategy
IV. EXPERIMENTS
A. Waveform Classification (Step 1)
B. Parameter Estimation (Step 2)
C. Full Pipeline Accuracy (Step 1, 2)
V. CONCLUSION
ACKNOWLEDGMENT
REFERENCES

키워드

저자

• Jaehyeok Yoon [ Department of Electrical and Electronic Engineering Hanyang University Ansan, South Korea ]
• Haewoon Nam [ Department of Electrical and Electronic Engineering Hanyang University Ansan, South Korea ] Corresponding Author
• Jaerock Kwon [ Department of Electrical and Computer Engineering University of Michigan-Dearborn Dearborn, MI, USA ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004