ID	原文	译文
53857	通过在OTB-50、OTB-100和OTB-2013三个基准数据集上进行了实验，验证了本文算法在复杂场景下更具有鲁棒性。	Experiments on OTB-50, OTB-100 and OTB-2013 benchmark datasets show that the proposed algorithm is more robust in complex scenes.
53858	为提高合成孔径雷达图像车辆目标的识别性能，本文提出一种SAR图像车辆目标多模态联合协同表示分类(Joint Multimode Cooperative Representation Classification，JMCRC)方法。	In order to improve the identification performance of vehicle targets in Synthetic Aperture Radar( SAR) images, this paper proposes a Joint Multimode Cooperative Representation Classification based Classification( JMCRC) method for SAR image vehicle targets.
53859	首先采用二维变分模态分解技术将SAR图像分解为分别表征全局信息和边缘信息的多个子模态分量，接着提取各子模态的二维双向主成分分析((2D)2PCA)特征;	Firstly, Two Dimensional Variational Mode Decomposition is used to decompose SAR image into multiple sub-modal components representing global information and edge information respectively, and then extracting the twodimensional bidirectional principal component analysis(( 2 D)2 PCA) characteristics from each sub-modal;
53860	其次将协同表示分类扩展为多模态联合协同表示分类，联合原始图像和各子模态的特征完成分类任务。	Secondly, the Cooperative Representation Classification was extended to the JMCRC, and the original image and features of each sub-mode were combined for the Classification task.
53861	在MSTAR数据集和实测数据集上对所提方法进行了验证，结果表明该方法在标准操作条件(Standard Operating Condition，SOC)以及两种型号差异条件、俯仰角变化条件和样本不平衡条件中均取得更好的分类性能。	The proposed method is verified on the MSTAR dataset and a real recorded dataset, and the results show that the method proposed in this paper achieves better classification performance under the Standard Operating Condition( SOC), specific model recognition, depression angle variance and ample unbalanced experimental conditions.
53862	针对宽带线性调频信号的采集、存储和传输困难问题，提出了一种基于α-随机解调器的线性调频信号模拟信息转换方法，有效降低信号采集频率与采样点数。	In order to solve the problem of acquisition, storage and transmission for linear frequency modulated(LFM) signals, a novel analog-to-information conversion method based on α-random demodulator is proposed. With the proposed system, both the sampling frequency and sample number can be reduced significantly during the compressive sampling.
53863	首先，根据线性调频信号特点，提出了α-随机解调器的系统模型，用以完成对线性调频信号的压缩采样。	Utilizing the sparsity of LFM signals in fractional Fourier domain, the compressive sampling process was achieved by the proposed α-random demodulator.
53864	然后，分析了α-随机解调器工作过程，建立了系统采样点与原始输入信号稀疏系数之间的联系，	Then, we analyzed the working process for α-random demodulator systematically. The relationship between system output and sparse vector of LFM signals was presented.
53865	进而建立了α-随机解调器压缩观测数学模型。基于该模型，分析了稀疏系数精确重构条件以及原始信号重构模型。	And based on this relationship, we further established the mathematical reconstruction model for α-random demodulator to reconstruct the sparse vector of LFM signals.
53866	最后，通过引入基于凸优化的信号重构算法，实现了对原始信号的准确重构。	Finally, the reconstruction was achieved by solving the convex optimization problem. And the accurate reconstruction for original signals is obtained.