ID	原文	译文
8464	这对于进一步提高三元组射频仿真精度具有理论意义和实用价值。	To further improve the triple rf simulation accuracy has theoretical significance and practical value.
8465	提出了一种基于信息融合的目标管理与被动定位技术的总体思路与实现方法。	This paper proposes a management by objectives based on information fusion and overall thinking and implementation method of passive location technology.
8466	以坦克分队对目标的管理和被动定位需求为应用背景,首先利用战场图像信息提取目标种类、位置和姿态特征,并依次通过对目标3类特征的对比判定,解决了被动定位前对不同坦克瞄准镜视场中目标是否为同一目标的判定问题,实现了对敌方目标的全局统一编号与动态管理;	Of target management by tank brigade and passive positioning demand as application background, the first use of battlefield image information to extract the characteristics, types, the position and posture, and so on characteristics, three kinds of contrast, solved the passive positioning of different tanks before sight field goals are for the same target decision problem, to realize the global uniform number of enemy targets with dynamic management;
8467	其次利用坦克自身位置和测得的目标方位、俯仰等角度信息,推导了目标位置坐标的计算公式,分析了计算误差的主要影响因素;	Second USES the location of the tanks and the measured target azimuth and pitching Angle information, such as target coordinates calculation formula was deduced and analyzed the main influence factors of calculation error;
8468	最后利用坦克分队网络化协同作战的优势,提出了一种以目标位置估计误差最小为约束条件的目标被动定位系统误差融合估计与补偿算法,显著提高了目标被动定位的精度,并通过仿真和野外实验,验证了所提方法的有效性。	Finally, taking advantage of the tank brigade networked cooperative engagement, this paper proposes a the minimum target location estimation error as the constraint condition of fusion target passive positioning system error estimation and compensation algorithm, significantly improve the precision of target passive localization, and through the simulation and field experiment, verify the effectiveness of the proposed method.
8469	针对变换域通信系统中干扰信号的分类识别问题,提出了一种基于信号特征空间的支持向量机(signal feature space-support vector machine,SF-SVM)干扰分类算法。	Aiming at the classification of the jamming signal in transform domain communication system identification problems, put forward a kind of support vector machine (SVM) based on signal feature space (signal feature space - support vector machine, SF - SVM) classification algorithm.
8470	首先,基于干扰信号模型和信号空间理论对干扰信号进行特征提取,并建立信号特征空间,进而针对二分类和多分类问题提出了SF-SVM分类算法,设计了干扰信号的多分类识别器。	First of all, based on the theory of interference signals model and spatial characteristics of the jamming signal are extracted, and establish the signal feature space, and then put forward for binary classification and classification problem SF - SVM classification algorithm, design of the jamming signal classification recognizer.
8471	仿真结果表明,与干扰信号的传统分类算法相比,SF-SVM不仅提高了分类精度,而且缩短了训练时间;	The simulation results show that compared with the traditional classification algorithm interfering signal, SF - SVM not only improves the classification accuracy, but also shortens the training time;
8472	设计的多分类识别器在信噪比达到8dB时,对6种干扰信号识别性能及对变换域通信系统性能都有所提升。	Design of classification recognizer in signal-to-noise ratio reaches 8 db, 6 kinds of jamming signal recognition on the performance and on the transform domain communication system performance are improved.
8473	针对多自主式水下潜器(autonomous underwater vehicle,AUV)在协同导航过程中量测异常等问题,提出一种基于交互式模型的多AUV协同导航滤波算法。	For multiple autonomous water diving (autonomous underwater vehicle, the AUV) measurement in the process of collaborative navigation problems such as abnormal, put forward a kind of multiple cooperating AUV navigation filtering algorithm based on the interactive model.