As a core part of the electronic warfare (EW) system, de-interleaving is used to separate interleaved radar signals. As interleaved radar pulses become more complex and denser, intelligent classification of radar signals has become very important. The self-organizing feature map (SOFM) is an excellent artificial neural network, which has huge advantages in intelligent classification of complex data. However, the de-interleaving process based on SOFM is faced with the problems that the initialization of the map size relies on prior information and the network topology cannot be adaptively adjusted. In this paper, an SOFM with self-adaptive network topology (SANT-SOFM) algorithm is proposed to solve the above problems. The SANT-SOFM algorithm first proposes an adaptive proliferation algorithm to adjust the map size, so that the initialization of the map size is no longer dependent on prior information but is gradually adjusted with the input data. Then, structural optimization algorithms are proposed to gradually optimize the topology of the SOFM network in the iterative process, constructing an optimal SANT. Finally, the optimized SOFM network is used for de-interleaving radar signals. Simulation results show that SANT-SOFM could get excellent performance in complex EW environments and the probability of getting the optimal map size is over 95% in the absence of priori information.

中文翻译：

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一种改进的基于SOFM的自适应网络拓扑结构的雷达脉冲解交织算法

作为电子战（EW）系统的核心部分，解交织用于分离交织的雷达信号。随着交错雷达脉冲变得越来越复杂和密集，雷达信号的智能分类变得非常重要。自组织特征图（SOFM）是一种优秀的人工神经网络，在复杂数据的智能分类方面具有巨大的优势。然而，基于SOFM的解交织过程面临着地图大小的初始化依赖于先验信息和网络拓扑不能自适应调整的问题。针对上述问题，本文提出了一种自适应网络拓扑结构的SOFM（SANT-SOFM）算法。SANT-SOFM算法首先提出了自适应增殖算法来调整地图大小，使得地图大小的初始化不再依赖于先验信息而是随着输入数据逐渐调整。然后提出结构优化算法，在迭代过程中逐步优化SOFM网络的拓扑结构，构建最优SANT。最后，优化的 SOFM 网络用于解交织雷达信号。仿真结果表明，SANT-SOFM能够在复杂的电子战环境中获得优异的性能，在没有先验信息的情况下获得最佳地图尺寸的概率超过95%。优化的 SOFM 网络用于解交织雷达信号。仿真结果表明，SANT-SOFM能够在复杂的电子战环境中获得优异的性能，在没有先验信息的情况下获得最佳地图尺寸的概率超过95%。优化的 SOFM 网络用于解交织雷达信号。仿真结果表明，SANT-SOFM能够在复杂的电子战环境中获得优异的性能，在没有先验信息的情况下获得最佳地图尺寸的概率超过95%。