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| Application of Anomaly Detection Algorithm in Fuze Interference Signal Recognition |
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| DOI：10.7643/issn.1672-9242.2022.11.006 |
| KeyWord:/Abstract7260.shtml.DONG Er-wa, HAO Xin-hong, YAN Xiao-peng, et al. Research on the Interference Mechanism of Swept Frequency Interference to UWB Radio Fuze [J/OL]. Acta Armamarii, 2022:1-10. http://www.co-journal.com/CN/ Abstract/Abstract7260.shtml. |
| Author | Institution |
| BAI Fan |
School of Equipment Engineering,Beijing , China |
| ZHANG Hui |
School of Mechanical Engineering, Shenyang Ligong University, Shenyang , China |
| LI Peng-fei |
School of Mechanical and Electrical Engineering, Beijing Institute of Technology, Beijing , China;Key Laboratory of Mechatronic Dynamic Control, Xi'an , China |
| CAO Zhao-rui |
School of Equipment Engineering,Beijing , China |
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| Abstract: |
| The paper intends to solve the problem of low calculation accuracycaused bythe difficult acquisition of jamming signals, the small number of characteristic signal samples and the imbalance of positive and negative samples in traditional classification fuze anti-jamming algorithm, overcome the dependenceon fuze anti-jamming algorithm on samplesand improve the accuracy of fuze signal recognition. Through the method of WVD time-frequency transformation, the split real jamming Fuze Signal slices are reorganized to expand fromaone-dimensional time-series signal to two-dimensional picture information. The data multiplication strategyis used to improvethe generalization of the algorithm and reduce its dependence on real data samples. Through thecombination of GANomaly and EfficientNet, the offline jamming signal feature learning is carried out on the expanded fuze data set, and the online abnormality judgment and jamming signal recognition are carried out on the image data of the disturbed fuze. The experiment proves that the GE-FS network can effectively augment the data on the basis of the real fuze small sample signal. After training based on the augmented data, the accuracy of fuze disturbance identification reaches 98.4%. The GE-FS algorithm can accurately detect and identify the abnormal signals of the fuze, and enhance the anti-jamming ability and operational adaptability of the fuze system. |
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