针对多功能雷达在信号层面分析时样式复杂多变、整体特征表述不全面、提供关键信息能力不足的问题,建立了一种多层级的多功能雷达行为层面表征模型,提出了一种基于一维深度卷积神经网络和门控循环网络并行处理的融合网络结构。在使用多层级模型清晰有效地表征和分析多功能雷达行为的基础上,结合两种网络分别在局部深度特征提取和全局时序特征提取方面的优势,实现了对多功能雷达典型功能的行为辨识。仿真实验结果表明,在参数交织程度较高的情况下,该网络对多功能雷达四种典型功能的行为辨识准确率达到95. 6%,证明了所提的并行网络算法在侦察情报分析领域具有良好的应用前景。

A multi-level multifunctional radar behavior level representation model is established to address the problems of complex and varied styles, incomplete overall feature representation, and insufficient ability to provide key information in signal level analysis of multifunctional radar. A fusion network structure based on parallel processing of one-dimensional deep convolution neural network and gated recurrent unit is proposed. On the basis of using multi-level models to clearly and effectively characterize and analyze the behavior of multifunctional radar, combined with the advantages of two networks in local depth feature extraction and global time-series feature extraction, the behavior identification of typical functions of multifunctional radar has been achieved. The simulation experiment results show that, with a high degree of parameter interleaving, the network achieves a behavior recognition accuracy of 95. 6% for the four typical functions of multifunctional radar, which proves that the proposed parallel network algorithm has good application prospects in the field of reconnaissance intelligence analysis.

TN957.51