Adaptive feature extraction and fine-grained modulation recognition of multi-function radar under small sample conditions

Qihang Zhai, Yan Li*, Zilin Zhang, Yunjie Li, Shafei Wang

*此作品的通讯作者

科研成果: 期刊稿件文章同行评审

9 引用 (Scopus)

摘要

Multi-function radars (MFRs) are sophisticated sensors with fine-grained modes, which modify their modulation types and parameters range generating various signals to fulfil different tasks, such as surveillance and tracking. In electromagnetic reconnaissance, recognition of MFR fine-grained modes can provide a basis for analysing strategies and reaction. With the limit of real applications, it is hard to obtain a large number of labelled samples for existing methods to learn the difference between categories. Therefore, it is essential to develop new methods to extract general knowledge of MFRs and identify modes with only a few samples. This paper proposes a few-shot learning (FSL) framework based on efficient neural architecture search (ENAS) with high robustness and portability, which designs a suitable network structure automated and quickly adapts to new environments. The experimental results show that the proposed method can still achieve excellent fine-grained modulation recognition performance (92.6%) under the condition of -6 dB signal-to-noise ratio (SNR), even when each class only provides one fixed-duration signal sample. The robustness is also verified under different conditions.

源语言英语
页(从-至)1460-1469
页数10
期刊IET Radar, Sonar and Navigation
16
9
DOI
出版状态已出版 - 9月 2022

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