Academic Journal
FuseCLIP: Semantic-Guided Multidomain Fusion for Few-Shot Radar Active Jamming Recognition.
| Τίτλος: | FuseCLIP: Semantic-Guided Multidomain Fusion for Few-Shot Radar Active Jamming Recognition. |
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| Συγγραφείς: | Yang, Zongyuan, Wen, Wei, Kong, Yukai, Wang, Xiang, Huang, Zhixiang, Cheng, Guangshang |
| Πηγή: | Remote Sensing; Sep2026, Vol. 18 Issue 17, p2967, 21p |
| Θεματικοί όροι: | Multisensor data fusion, Classification algorithms, Time-domain analysis, Machine learning, Radar interference, Time-frequency analysis, Frequency-domain analysis |
| Περίληψη: | Highlights: What are the main findings? FuseCLIP combines time-domain, frequency-domain, and STFT features for few-shot radar active jamming recognition. It compares fused signal features with CLIP text prototypes for classification and improves recognition under different shot numbers and JNR levels. What are the implications of the main finding? Multidomain features provide complementary information for recognizing jamming types with limited labeled samples. CLIP text prototypes add semantic information and support radar anti-jamming recognition in complex electromagnetic environments. Radar active jamming recognition is an essential component of radar anti-jamming processing and cognitive radar decision making. As jamming categories proliferate and electromagnetic environments become more complex, collecting sufficient labeled samples for every possible jamming condition can be challenging in practical scenarios. Thus, it becomes necessary to exploit the recognition capability under few-shot conditions, especially in the face of varying jamming-to-noise ratio (JNR) and compound interference. Existing methods commonly rely on a single signal representation or treat jamming categories as discrete labels, which may underuse the complementary evidence available across signal domains and the semantic knowledge associated with jamming categories. This paper proposes FuseCLIP, a semantic-guided multidomain fusion framework for few-shot radar active jamming recognition. FuseCLIP jointly encodes time-domain sequences, frequency-domain sequences, and short-time Fourier transform (STFT) spectrograms to capture waveform, spectral, and time-varying spectral characteristics. The resulting features are adaptively fused and mapped into a unified representation space, where they are matched with fixed Contrastive Language–Image Pre-training (CLIP) text prototypes generated from task-related jamming descriptions. By combining multidomain physical information with category-level semantic priors, the proposed framework is intended to make more effective use of limited labeled data. Experiments on a simulated radar active jamming dataset under different shot numbers and JNR levels demonstrate the effectiveness of FuseCLIP relative to representative baselines. Ablation experiments further indicate the complementary contributions of the time-domain, frequency-domain, and time-frequency inputs. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: | Complementary Index |
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