| ID | 原文 | 译文 |
| 53807 | 在标准动态纹理数据集上的分类实验结果表明，本文方法具有良好的分类性能。 | The experiment results on standard dynamic texture data sets show that the proposed method has good classification performance. |
| 53808 | 利用卷积神经网络对目标微多普勒特征进行深度学习是目前雷达探测无人机分类的重要手段。 | It is an important method for classifying UAVs detected by radar using convolutional neural networks to perform deep learning on targets' micro-Doppler features. |
| 53809 | 实际应用中，无人机参数如叶片转速、叶片长度、叶片初始相位、无人机方位角、无人机俯仰角、无人机径向速度等参数变化大，导致训练样本变化大。 | Actually, parameters of UAVs such as blade rotation speed, blade length, blade initial phase, UAVs' azimuth, UAVs' pitch angle, and UAVs' radial velocity, etc. vary greatly, which leads to large variation in training samples sets. |
| 53810 | 该文分析训练样本集对旋翼无人机分类结果的影响。 | In this paper, impacts of training samples sets on rotor UAVs' classification results are analyzed. |
| 53811 | 首先建立单旋翼无人直升机、四旋翼无人机和六旋翼无人机雷达回波仿真模型。 | Firstly, simulated radar echoes models of helicopters, quadrotors and hexarotors are established. |
| 53812 | 然后对其进行微多普勒特征分析提取，构建多种不同情况下的合并多普勒图像(Merged Doppler Images，MDI)训练样本集。 | Then micro-Doppler features analysis and extraction are carried out, and Merged Doppler Images(MDI) training samples sets are constructed in many different situations. Finally, GoogLeNet(Inception v1) is used to obtain UAVs' classification results in different situations. |
| 53813 | 最后利用GoogLeNet (Inception v1)得到不同情况下的无人机分类结果，分析训练样本集中样本数量、无人机单一参数变化、样本参数涵盖完整性以及无人机参数采样间隔对分类准确率的影响。 | Impacts of sample quantity, variation of UAVs' single parameter, completeness of sample parameters coverage and sampling intervals of UAVs' parameters of training sets on the classification accuracy are analyzed. |
| 53814 | 实验结果表明：训练样本集的差异可能对分类准确率产生显著影响。 | The experiment results show that the difference in MDI training sets may have significant impacts on UAVs' classification accuracy. |
| 53815 | 振动传感器接收的信号往往包含不同部件的振动信号和环境噪声，为了从少量振动传感器的接收信号中识别信号源数和各频率分量，提出了一种基于稀疏分量分析的欠定盲源分离方法。 | The signals received by vibration sensors often contain vibration signals of different components and environmental noise. In order to identify the number of signal sources and various frequency components from the received signals of a small number of vibration sensors, an underdetermined blind source separation method based on sparse component analysis is proposed. |
| 53816 | 该方法首先对混合信号进行时频变换，通过主成分分析提取各个时频点邻域的局部主成分，筛选出单源域特征数据。 | This method first performs time-frequency transformation on the mixed signal, extracts the local principal components of the neighborhood of each time-frequency point through principal component analysis, and filters out the single-source domain feature data. |