Weak Labeling-specific Emitter Identification Algorithm Based on the Weakly Supervised Wav-KAN Network
Most existing specific emitter identification technologies rely on supervised learning, making them unsuitable for scenarios with label loss due to factors such as the acquisition environment (e.g., weather conditions, terrain, obstacles, and interference sources), device performance (e.g., radar re...
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| Format: | Article |
| Language: | English |
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China Science Publishing & Media Ltd. (CSPM)
2025-04-01
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| Series: | Leida xuebao |
| Subjects: | |
| Online Access: | https://radars.ac.cn/cn/article/doi/10.12000/JR24248 |
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| _version_ | 1849337910790193152 |
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| author | Kangsheng LIU Qing LING Wenjun YAN Limin ZHANG Keyuan YU Hengyan LIU |
| author_facet | Kangsheng LIU Qing LING Wenjun YAN Limin ZHANG Keyuan YU Hengyan LIU |
| author_sort | Kangsheng LIU |
| collection | DOAJ |
| description | Most existing specific emitter identification technologies rely on supervised learning, making them unsuitable for scenarios with label loss due to factors such as the acquisition environment (e.g., weather conditions, terrain, obstacles, and interference sources), device performance (e.g., radar resolution, signal processing capabilities, and hardware failures), and tagger level. In this study, a weakly labeled specific emitter identification algorithm based on the Weakly Supervised Wav-KAN (WSW-KAN) network is proposed. First, a WSW-KAN baseline network is constructed by integrating the unique learnable edge function of the KAN network with the multiresolution analysis of the wavelet function. The weakly labeled dataset is then divided into a small labeled dataset and a large unlabeled dataset, with the small labeled dataset used for initial model training. Finally, based on the pretrained model, Adaptive Pseudo-Label Weighted Selection (APLWS) is used to extract features from the unlabeled data using a contrast learning method, followed by iterative training, thereby effectively improving the generalization capability of the model. Experimental validation using a real acquisition radar dataset demonstrates that the proposed algorithm achieves a recognition accuracy of approximately 95% for specific emitters while maintaining high efficiency, a small parameter scale, and strong adaptability, making it suitable for practical applications. |
| format | Article |
| id | doaj-art-75767846a4fa41999c30a8a5ffcc7165 |
| institution | Kabale University |
| issn | 2095-283X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | China Science Publishing & Media Ltd. (CSPM) |
| record_format | Article |
| series | Leida xuebao |
| spelling | doaj-art-75767846a4fa41999c30a8a5ffcc71652025-08-20T03:44:33ZengChina Science Publishing & Media Ltd. (CSPM)Leida xuebao2095-283X2025-04-0114233835210.12000/JR24248R24248Weak Labeling-specific Emitter Identification Algorithm Based on the Weakly Supervised Wav-KAN NetworkKangsheng LIU0Qing LING1Wenjun YAN2Limin ZHANG3Keyuan YU4Hengyan LIU5Institute of Information Fusion, Naval Aviation University, Yantai 264001, ChinaInstitute of Information Fusion, Naval Aviation University, Yantai 264001, ChinaInstitute of Information Fusion, Naval Aviation University, Yantai 264001, ChinaInstitute of Information Fusion, Naval Aviation University, Yantai 264001, ChinaInstitute of Information Fusion, Naval Aviation University, Yantai 264001, ChinaInstitute of Information Fusion, Naval Aviation University, Yantai 264001, ChinaMost existing specific emitter identification technologies rely on supervised learning, making them unsuitable for scenarios with label loss due to factors such as the acquisition environment (e.g., weather conditions, terrain, obstacles, and interference sources), device performance (e.g., radar resolution, signal processing capabilities, and hardware failures), and tagger level. In this study, a weakly labeled specific emitter identification algorithm based on the Weakly Supervised Wav-KAN (WSW-KAN) network is proposed. First, a WSW-KAN baseline network is constructed by integrating the unique learnable edge function of the KAN network with the multiresolution analysis of the wavelet function. The weakly labeled dataset is then divided into a small labeled dataset and a large unlabeled dataset, with the small labeled dataset used for initial model training. Finally, based on the pretrained model, Adaptive Pseudo-Label Weighted Selection (APLWS) is used to extract features from the unlabeled data using a contrast learning method, followed by iterative training, thereby effectively improving the generalization capability of the model. Experimental validation using a real acquisition radar dataset demonstrates that the proposed algorithm achieves a recognition accuracy of approximately 95% for specific emitters while maintaining high efficiency, a small parameter scale, and strong adaptability, making it suitable for practical applications.https://radars.ac.cn/cn/article/doi/10.12000/JR24248specific emitter identification (sei)weakly supervised wav-kan (wsw-kan)pseudo-label iterationweakly supervised learningcontrastive learningneural network |
| spellingShingle | Kangsheng LIU Qing LING Wenjun YAN Limin ZHANG Keyuan YU Hengyan LIU Weak Labeling-specific Emitter Identification Algorithm Based on the Weakly Supervised Wav-KAN Network Leida xuebao specific emitter identification (sei) weakly supervised wav-kan (wsw-kan) pseudo-label iteration weakly supervised learning contrastive learning neural network |
| title | Weak Labeling-specific Emitter Identification Algorithm Based on the Weakly Supervised Wav-KAN Network |
| title_full | Weak Labeling-specific Emitter Identification Algorithm Based on the Weakly Supervised Wav-KAN Network |
| title_fullStr | Weak Labeling-specific Emitter Identification Algorithm Based on the Weakly Supervised Wav-KAN Network |
| title_full_unstemmed | Weak Labeling-specific Emitter Identification Algorithm Based on the Weakly Supervised Wav-KAN Network |
| title_short | Weak Labeling-specific Emitter Identification Algorithm Based on the Weakly Supervised Wav-KAN Network |
| title_sort | weak labeling specific emitter identification algorithm based on the weakly supervised wav kan network |
| topic | specific emitter identification (sei) weakly supervised wav-kan (wsw-kan) pseudo-label iteration weakly supervised learning contrastive learning neural network |
| url | https://radars.ac.cn/cn/article/doi/10.12000/JR24248 |
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