Radar Compound Jamming Recognition Based on Image Segmentation and Fused Attention Residual Network
With the increasing complexity of modern electromagnetic environments, radar systems are not only affected by single jamming signals but also by compound jamming, which consists of additive combinations of multiple jamming types. Compound jamming is difficult to recognize due to a wide array of dive...
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| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/7/2124 |
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| author | Peishan Li Jian Yang Jiaao Lin |
| author_facet | Peishan Li Jian Yang Jiaao Lin |
| author_sort | Peishan Li |
| collection | DOAJ |
| description | With the increasing complexity of modern electromagnetic environments, radar systems are not only affected by single jamming signals but also by compound jamming, which consists of additive combinations of multiple jamming types. Compound jamming is difficult to recognize due to a wide array of diverse compound patterns. To address this issue, this study proposes a method for the segmentation and recognition of compound jamming signals. First, a jamming segmentation module based on image segmentation techniques is designed to segment the compound jamming in the time–frequency domain, which is obtained by short-time Fourier transform (STFT). Subsequently, an enhanced residual network (ResNet) incorporating a spatial-channel fused attention mechanism (SCFAM) is proposed to effectively capture multi-level features and recognize the segmented jamming signals. The experimental results demonstrate that the proposed method achieves a recognition accuracy of 98.60% for compound jamming, outperforming three classical approaches. Additionally, this method exhibits superior performance in recognizing untrained types of compound jamming, highlighting its robustness and generalization capability. |
| format | Article |
| id | doaj-art-41c2ec00e40d485798f177d83072efa9 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-41c2ec00e40d485798f177d83072efa92025-08-20T03:03:24ZengMDPI AGSensors1424-82202025-03-01257212410.3390/s25072124Radar Compound Jamming Recognition Based on Image Segmentation and Fused Attention Residual NetworkPeishan Li0Jian Yang1Jiaao Lin2China Academy of Launch Vehicle Technology, No. 165, South 4th Ring East Road, Fengtai District, Beijing 100076, ChinaChina Academy of Launch Vehicle Technology, No. 165, South 4th Ring East Road, Fengtai District, Beijing 100076, ChinaChina Academy of Launch Vehicle Technology, No. 165, South 4th Ring East Road, Fengtai District, Beijing 100076, ChinaWith the increasing complexity of modern electromagnetic environments, radar systems are not only affected by single jamming signals but also by compound jamming, which consists of additive combinations of multiple jamming types. Compound jamming is difficult to recognize due to a wide array of diverse compound patterns. To address this issue, this study proposes a method for the segmentation and recognition of compound jamming signals. First, a jamming segmentation module based on image segmentation techniques is designed to segment the compound jamming in the time–frequency domain, which is obtained by short-time Fourier transform (STFT). Subsequently, an enhanced residual network (ResNet) incorporating a spatial-channel fused attention mechanism (SCFAM) is proposed to effectively capture multi-level features and recognize the segmented jamming signals. The experimental results demonstrate that the proposed method achieves a recognition accuracy of 98.60% for compound jamming, outperforming three classical approaches. Additionally, this method exhibits superior performance in recognizing untrained types of compound jamming, highlighting its robustness and generalization capability.https://www.mdpi.com/1424-8220/25/7/2124compound jammingjamming recognitionresidual networkattention mechanism |
| spellingShingle | Peishan Li Jian Yang Jiaao Lin Radar Compound Jamming Recognition Based on Image Segmentation and Fused Attention Residual Network Sensors compound jamming jamming recognition residual network attention mechanism |
| title | Radar Compound Jamming Recognition Based on Image Segmentation and Fused Attention Residual Network |
| title_full | Radar Compound Jamming Recognition Based on Image Segmentation and Fused Attention Residual Network |
| title_fullStr | Radar Compound Jamming Recognition Based on Image Segmentation and Fused Attention Residual Network |
| title_full_unstemmed | Radar Compound Jamming Recognition Based on Image Segmentation and Fused Attention Residual Network |
| title_short | Radar Compound Jamming Recognition Based on Image Segmentation and Fused Attention Residual Network |
| title_sort | radar compound jamming recognition based on image segmentation and fused attention residual network |
| topic | compound jamming jamming recognition residual network attention mechanism |
| url | https://www.mdpi.com/1424-8220/25/7/2124 |
| work_keys_str_mv | AT peishanli radarcompoundjammingrecognitionbasedonimagesegmentationandfusedattentionresidualnetwork AT jianyang radarcompoundjammingrecognitionbasedonimagesegmentationandfusedattentionresidualnetwork AT jiaaolin radarcompoundjammingrecognitionbasedonimagesegmentationandfusedattentionresidualnetwork |