We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Low Probability of Intercept Radar Signal Recognition Based on Semi-Supervised Support Vector Machine.
- Authors
Xu, Fuhua; Hu, Haoning; Mu, Jiaqing; Wang, Xiaofeng; Zhou, Fang; Quan, Daying
- Abstract
Low probability of intercept (LPI) radar signal recognition under low signal-to-noise ratio (SNR) is a challenging task within electronic reconnaissance systems, particularly when faced with scarce labeled data and limited resources. In this paper, we introduce an LPI radar signal recognition method based on a semi-supervised Support Vector Machine (SVM). First, we utilize the Multi-Synchrosqueezing Transform (MSST) to obtain the time–frequency images of radar signals and undergo the necessary preprocessing operations. Then, the image features are extracted via Discrete Wavelet Transform (DWT), and the feature dimension is reduced by the principal component analysis (PCA). Finally, the dimensionality reduction features are input into the semi-supervised SVM to complete the classification and recognition of LPI radar signals. The experimental results demonstrate that the proposed method achieves high recognition accuracy at low SNR. When the SNR is −6 dB, its recognition accuracy reaches almost 100%.
- Subjects
SUPERVISED learning; FEATURE extraction; DISCRETE wavelet transforms; SUPPORT vector machines; PRINCIPAL components analysis
- Publication
Electronics (2079-9292), 2024, Vol 13, Issue 16, p3248
- ISSN
2079-9292
- Publication type
Article
- DOI
10.3390/electronics13163248