Intelligent identification of radar active jamming type based on multi-domain information fusion

• Published in: Journal of physics. Conference series 2023-04, Vol.2480 (1), p.12015
• Main Authors: Cao, Fei, Gao, Zejun, He, Chuan, Feng, Xiaowei, Xu, Jianfeng, Xue, Chunling, Qin, Jianqiang
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Data integration; Frequency spectrum; Jamming; Physics; Radar; Resource allocation
• ISSN: 1742-6588; 1742-6596
• DOI: 10.1088/1742-6596/2480/1/012015

Feature fusion is beneficial to improving the recognition rate of radar active jamming types, which usually costs a lot of computation and network complexity, however. Contrapose the disadvantage of low computational power and resource allocation of missile-borne radar, this paper introduces data enhancement theory into the feature fusion method and proposes an intelligent identification model of radar active jamming type based on multi-domain information fusion. The model first analyzes the time-frequency domain features of the signal and one-dimensional features such as its real part, imaginary part, frequency spectrum, and power spectrum, and then uses the Cutout&Patchup algorithm to fuse the one-dimensional and two-dimensional features into a new multi-domain information fusion matrix as the input of the recognition network. The simulation results show that this method greatly improves the recognition accuracy of active jamming types. Under the WideResNet28_2 classifier, the classification accuracy of active interference type reaches 88.06%, which is 0.79% higher than that before fusion.