Illegally forged signals in automatic identification system (AIS) pose a threat to maritime traffic safety management. In this paper, a multi-level sparse representation based identification (MSRI) algorithm is proposed for specific emitter identification (SEI) in the AIS. The MSRI innovatively combines neural networks with sparse representation based classification (SRC). Channel attention mechanism is introduced to a multi-scale convolutional neural network (CNN) for extracting hidden features in the signal. These extracted features are divided into shallow and deep features according to the depth of the network layer they are extracted from. The original AIS signals and the two-level features are spliced together to form a multi-level dictionary. Subsequently, a sparse representation based identification is performed on the decorrelated multi-level dictionary using the principal components analysis (PCA) method. The proposed MSRI is evaluated on a dataset composed of real-world AIS signals, and compared with the state-of-the-art identification algorithms. The evaluation is based on several factors including computational complexity, number of training samples, and number of emitters. Numerical results indicate that the proposed algorithm can identify emitters with higher accuracy and requires lower training time compared to other methods. Given more than 15 training samples at each emitter, the MSRI can identify nine emitters with an accuracy higher than 90%.

中文翻译：

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自动识别系统中基于多级稀疏表示的特定辐射源识别

自动识别系统（AIS）中非法伪造的信号对海上交通安全管理构成了威胁。本文针对AIS中的特定发射器识别（SEI）提出了一种基于多级稀疏表示的识别（MSRI）算法。MSRI创新地将神经网络与基于稀疏表示的分类（SRC）结合在一起。通道注意机制被引入到多尺度卷积神经网络（CNN）中，以提取信号中的隐藏特征。这些提取的特征根据提取它们的网络层的深度分为浅层特征和深层特征。原始AIS信号和两级特征被拼接在一起以形成多级字典。随后，使用主成分分析（PCA）方法对去相关的多级字典执行基于稀疏表示的识别。建议的MSRI在包含真实AIS信号的数据集上进行评估，并与最新的识别算法进行比较。评估基于几个因素，包括计算复杂度，训练样本数和发射器数。数值结果表明，与其他方法相比，所提算法能够更准确地识别出辐射源，并且所需的训练时间更少。给定每个发射器有15个以上的训练样本，MSRI可以识别9个发射器，其准确性高于90％。建议的MSRI在包含真实AIS信号的数据集上进行评估，并与最新的识别算法进行比较。评估基于几个因素，包括计算复杂度，训练样本数和发射器数。数值结果表明，与其他方法相比，所提算法能够更准确地识别出辐射源，并且所需的训练时间更少。给定每个发射器有15个以上的训练样本，MSRI可以识别9个发射器，其准确度高于90％。建议的MSRI在包含真实AIS信号的数据集上进行评估，并与最新的识别算法进行比较。评估基于几个因素，包括计算复杂度，训练样本数和发射器数。数值结果表明，与其他方法相比，所提算法能够更准确地识别出辐射源，并且所需的训练时间更少。给定每个发射器有15个以上的训练样本，MSRI可以识别9个发射器，其准确性高于90％。数值结果表明，与其他方法相比，所提算法能够更准确地识别出辐射源，并且所需的训练时间更少。给定每个发射器有15个以上的训练样本，MSRI可以识别9个发射器，其准确性高于90％。数值结果表明，与其他方法相比，所提算法能够更准确地识别出辐射源，并且所需的训练时间更少。给定每个发射器有15个以上的训练样本，MSRI可以识别9个发射器，其准确性高于90％。