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摘要:
为提升复杂电磁环境战场中调频无线电引信的抗干扰能力，基于稀疏表示理论，将稀疏表示系数重构用于调频无线电的目标和干扰信号分类识别，提出一种目标信号和扫频式干扰信号的分类识别方法，解决了调频无线电引信的抗干扰能力不足的问题。采集了模拟目标及干扰信号作用于无线电引信的检波端输出信号，构建了目标信号过完备字典和干扰信号过完备字典，分别将测试信号在2类字典上进行稀疏分解并重构，依据重构误差对测试样本类别进行识别。结果表明：基于稀疏表示的调频无线电引信目标和干扰信号分类识别方法，可以对目标和干扰信号进行有效的识别，同时能够满足较低的虚警概率。研究成果对于调频无线电引信在复杂电磁环境中的抗干扰具有重要的借鉴意义。
Abstract:To enhance the frequency modulation radio fuze’s ability to prevent interference in the complex electromagnetic environment, a method of classifying and identifying the target and interference signals is suggested. This method is based on the sparse representation theory and uses the sparse representation coefficient reconstruction. The goal is to address the issue of the frequency modulation radio fuze’s insufficient ability to prevent interference. The output signals of the detector end of the radio fuze were collected, and the overcomplete dictionary of the target signal and the overcomplete dictionary of the interference signal were constructed. The test signals underwent sparse decomposition and reconstruction on both types of dictionaries. Based on the reconstruction errors, the test sample categories were determined. The results show that the classification and recognition method of frequency modulation radio fuze targets and interference signals based on sparse representation can effectively recognize targets and interference signals, and at the same time meet the lower false alarm probability. The research results have important reference significance for frequency modulation radio fuze’s anti-jamming and effective attack in complex electromagnetic environments.
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图 5 稀疏表示目标和干扰信号分类流程
Figure 5. Sparse representation of target and interference signal classification flow chart
图 6 目标和干扰信号KSVD字典可视化
Figure 6. Target and jamming signals KSVD dictionaries visualization
表 1 稀疏表示分类识别测试结果
Table 1. Sparse representation classification recognition test results
目标识别准确率$ {A_{\mathrm{t}}} $/% 干扰识别准确率$ {A_{\mathrm{j}}} $/% 被干扰概率$ {D_{\mathrm{r}}} $/% 97.60 97.54 2.46 
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