ID	原文	译文
8214	该方法利用二维间歇采样实现干扰信号的二维频谱周期性拓展,从而生成无法被SAR-GMTI完全对消的假目标群,并进一步提出采用慢时间延时转发来实现对假目标群方位向位移的控制。	The method using two-dimensional intermittent sampling disturbance signal of the two-dimensional spectrum periodic expansion, resulting in cannot be SAR - complete cancellation of GMTI false target group, and further forward time delay is proposed to implement the control of the false target group orientation to the displacement.
8215	在此基础上,针对SAR-GMTI的对消特性给出了多组延时转发干扰应用模型。	On this basis, in view of the SAR - GMTI cancellation feature more groups of delay forwarding interference application model is given.
8216	理论分析和仿真实验表明:该方法可对多通道SAR-GMTI产生二维假目标群,且假目标幅度受正弦调制函数的影响出现增强和削弱,多组延时转发干扰方法可以实现增强区与削弱区互补,从而形成分布更密集、覆盖范围更广的二维多假目标干扰效果。	Theory analysis and simulation results show that the method on the multi-channel SAR - GMTI, two-dimensional false target group, and false targets by sinusoidal amplitude modulation function enhancement and weaken the influence of group delay forwarding jamming method can realize more enhanced area and weaken the area complement each other, forming a more intensive distribution, covering a wider range of 2 d multiple false targets jamming effect.
8217	对于移动机器人研究领域来说,现阶段研究热点是如何在全球定位系统失效的情况下同时定位与地图构建(simultaneous localization and mapping,SLAM)。	For mobile robot research field, the research focus at this stage is how in the case of global positioning system (GPS) failure localization and map building at the same time (simultaneous localization and mapping, SLAM).
8218	对于单个机器人SLAM已经有很多解决方案,然而当转移到多机器人平台时,对于存在的问题又面临很多新的挑战。	Have many solutions for a single robot SLAM, when transferred to the robot platform, however, for the existing problems and facing many new challenges.
8219	本文首先分析了多机器人SLAM,着重探讨了多机器人SLAM后端优化算法。	This paper first analyzes the multi-robot SLAM, emphatically discusses the multi-robot SLAM the back-end optimization algorithm.
8220	分析了多机器人SLAM研究过程中遇到的不同问题,以及现阶段这些问题的处理算法。	Analyzed the multi-robot SLAM of different problems in the research process, and the current problems of processing algorithms.
8221	讨论了多机器人SLAM中扩展卡尔曼滤波、扩展信息滤波、粒子滤波、基于图优化的SLAM、地图融合等后端优化算法的研究现状,分析了算法的优缺点,并提出了未来发展的方向。	Discusses the multi-robot SLAM in extended kalman filtering, extended information filtering and particle filtering, SLAM based on graph optimization, such as map fusion research status quo of the back-end optimization algorithm, the advantages and disadvantages of the algorithm is analyzed, and the direction of future development are put forward.
8222	针对一种可快速搜索和寻找到聚类密度峰值点聚类算法的缺陷,利用线性回归与残差分析的方法进行改进,可自动、快速地确定聚类中心且优化样本点密度值。	For a quick search and find the clustering density peak point defects of clustering algorithm, the method of using linear regression and residual analysis was improved, can automatically and quickly determine the clustering center and the optimization of sample points density values.
8223	算法利用样本点的近邻信息重新度量点的密度值,提高聚类中心点位置稳定性;	Algorithm using neighbor information of sample points to measure point of density value, improve the clustering center position stability;