Abstract. Our work aims to address the problem of estimating the parameters of constant-amplitude, time-unsynchronized linear frequency-modulated (LFM) signals that have single or multiple components, which is a crucial task in electronic countermeasure techniques. A method for estimating the parameters, center frequency f0, and chirp rate μ of an LFM signal is proposed; the method is referred to as the Wigner–Ville distribution complex-valued convolutional neural network (WVD-CV-CNN). The method can be regarded as an application of neural networks for extracting parameter features from the signal spectrogram, wherein the CV-CNN is the main body of the network, which takes a complex-valued WVD matrix as the input and outputs several sets of estimated parameters. A performance analysis shows that the estimation accuracy and computational efficiency of the proposed method are significantly improved compared with those of the conventional methods. Further, the proposed method shows strong robustness to changes in modulation parameters. We apply the CV-CNN to other spectrograms and prove compatibility of the WVD and CV-CNN by comparison. We also demonstrate that the estimation accuracy of the proposed method is robust against cross interference on the WVD. Our study shows the advantages of using deep learning systems in signal parameter estimation.

中文翻译：

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基于Wigner-Ville分布复值卷积神经网络的线性调频信号参数估计

摘要。我们的工作旨在解决估计具有单个或多个分量的恒定幅度、时间不同步线性调频 (LFM) 信号的参数的问题，这是电子对抗技术中的一项关键任务。提出了一种估计LFM信号的参数、中心频率f0和线性调频率μ的方法；该方法被称为 Wigner-Ville 分布复值卷积神经网络 (WVD-CV-CNN)。该方法可以看作是神经网络从信号谱中提取参数特征的一种应用，其中以CV-CNN为网络主体，以复值WVD矩阵为输入，输出多组估计参数。性能分析表明，与传统方法相比，该方法的估计精度和计算效率都有显着提高。此外，所提出的方法对调制参数的变化表现出很强的鲁棒性。我们将 CV-CNN 应用于其他频谱图，并通过比较证明 WVD 和 CV-CNN 的兼容性。我们还证明了所提出方法的估计精度对 WVD 上的交叉干扰具有鲁棒性。我们的研究显示了在信号参数估计中使用深度学习系统的优势。我们将 CV-CNN 应用于其他频谱图，并通过比较证明 WVD 和 CV-CNN 的兼容性。我们还证明了所提出方法的估计精度对 WVD 上的交叉干扰具有鲁棒性。我们的研究显示了在信号参数估计中使用深度学习系统的优势。我们将 CV-CNN 应用于其他频谱图，并通过比较证明 WVD 和 CV-CNN 的兼容性。我们还证明了所提出方法的估计精度对 WVD 上的交叉干扰具有鲁棒性。我们的研究显示了在信号参数估计中使用深度学习系统的优势。