| Citation: | LANG B，WANG H，GONG J. A small sample data-driven radar compound jamming lightweight perception network[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（3）：1005-1014 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0343
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Radar jamming perception technology based on deep learning can accurately perceive all kinds of radar jamming types, but large-scale and complete training samples need to be constructed in advance. The workload and difficulty of data set construction are large. At the same time, there are some problems such as a large amount of network model parameters and high computational complexity, which make it difficult to apply in the actual platform. This research proposes a lightweight perception network powered by tiny sample data for radar compound jamming in order to overcome this challenge. For the first time, the jamming perception network is established combined with the idea of "target detection" in the field of computer vision. The multi-scale feature map is extracted by using the radar jamming time-frequency distribution data, and the anchor is preset for regression and classification. Secondly, the network structure with large parameters and high computational load is lightweight and improved by using group convolution and ghost convolution. The experimental results show that only a small-scale single jamming mode sample training can realize the flexible perception of single jamming mode, pairwise compound mode and three types of compound mode. The model has a considerably compressed number of parameters and processes while maintaining strong perception performance in the case of a low jamming noise ratio.
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