ID	原文	译文
53797	仿真结果表明，在未知杂波观测场景中，MHT-KDE算法有效改善了航迹的连续性，减少了虚假航迹数。	Simulation results show that MHT-KDE algorithm can effectively improve the track continuity and reduce the number of false tracks in unknown clutter observation scene.
53798	现有基于深度神经网络的辐射源识别算法受训练场景限制，当待测信号与训练数据集的信道环境噪声不一致时，网络的识别性能严重退化。	Existing deep neural network(DNN) based specific emitter identification(SEI) algorithms are limited by training scenarios, and the identification accuracy of the network is seriously degraded when the signal to be identified is not consistent with the channel noise of the training data.
53799	为了克服该问题，本文提出一种基于迁移学习的辐射源个体识别算法。	In order to solve this problem, this paper proposes a SEI algorithm based on transfer learning.
53800	该算法结合领域自适应的思想，建立优化模型将不同信噪比下信号的特征对齐，使在特定信噪比下训练的神经网络学习到与信道噪声无关的射频指纹特征，实现对其他信噪比下信号的高准确率识别。	Combining with the idea of domain adaptation, this method established an optimization model to align the features of signals under different signal-to-noise ratio(SNR), so that the neural network trained under a specific SNR can learn the radio frequency fingerprint(RFF) features which are independent of channel noise, and realize the identification of signals under other SNR conditions with high accuracy.
53801	仿真实验结果表明，提出的算法显著提升了基于深度神经网络的辐射源个体识别算法在动态噪声条件下的准确率，	Simulation results show that the proposed algorithm improves the accuracy of the SEI algorithm based on DNN under the interference of dynamic noise.
53802	在待识别信号信噪比下降4 dB的情况下，准确率提升了45.18%。	When the SNR of the signal to be identified decreases by 4 dB, the identification accuracy can be improved by 45. 18%.
53803	动态纹理在空间和时间上表现出“外观”和“运动”属性，为了有效结合这两种属性进行动态纹理分析，本文提出一种基于时间—顶点谱图小波变换与边缘分布协方差模型的动态纹理分类方法。	Dynamic textures exhibit “appearance” and “motion” properties in space and time. Combining these two properties, a dynamic texture classification method based on spectral time-vertex wavelet transform and Marginal distribution covariance model was proposed in this paper.
53804	该方法将动态纹理看成时间—顶点图信号，利用时间—顶点谱图Meyer小波变换对动态纹理进行多尺度分解，	The time-vertex graph signal processing framework was used to represent the dynamic texture as the time-vertex graph signal. Since Meyer wavelet can represent dynamic textures in multiple directions and at multiple scales, so the multi-scale decomposition of dynamic texture was performed by spectral time-vertex Meyer wavelet transform.
53805	再对每个子带应用边缘分布协方差模型，由此得到带内相关性的特征协方差矩阵作为动态纹理特征进行分类。	Then, Marginal distribution covariance model was applied to each sub-band, and the characteristic covariance matrix of intra-band correlation was obtained as dynamic texture feature for classification.
53806	由于时间—顶点图信号的表示可以有效描述动态纹理像素间的空间关系及其沿时间的变化，同时谱图小波变换继承了图表示和小波变换的优势，因此利用时间—顶点谱图小波分解与边缘分布协方差模型，可得到有效的动态纹理特征。	Due to the representation of time-vertex graph can effectively describe the spatial relations among dynamic texture pixels and their changes along time, meanwhile, spectral wavelet transform inherits the advantages of graph representation and wavelet transform, so we used spectra time-vertex wavelet decomposition and marginal distribution covariance model to obtain dynamic texture feature effectively.