Name
Abstract | ||
|---|---|---|
Accurate sensing of the mainlobe active deception jamming is critical for radar antijamming and extended target detection in a complex electromagnetic environment. This letter, therefore, deals with the problem of multiple active deception jamming recognition in extended target settings. A residual convolutional neural network (CNN) with an attention mechanism-based radar active deception jamming recognition algorithm is proposed, leveraging a hybrid model to capture many rich features through multidomain feature fusion. The proposed method can outperform state-of-the-art methods in terms of recognition accuracy, model size (MS), and convergence speed. Experimental results demonstrate its effectiveness and robustness. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/LGRS.2022.3184997 | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS |
Keywords | DocType | Volume |
Jamming, Radar, Feature extraction, Time-frequency analysis, Target recognition, Time-domain analysis, Frequency modulation, Active deception jamming recognition, attention mechanism and detection-recognition integration for jamming, extended target settings, multidomain feature fusion | Journal | 19 |
ISSN | Citations | PageRank |
1545-598X | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yukai Kong | 1 | 0 | 0.68 |
| Xiang Wang | 2 | 26 | 15.33 |
| Changxin Wu | 3 | 0 | 0.34 |
| Xianxiang Yu | 4 | 29 | 11.97 |
| Guolong Cui | 5 | 2 | 3.76 |