ID	原文	译文
8414	仿真结果表明了制导律具有更高的制导精度并对高速目标机动具有鲁棒性,验证了有限时间收敛条件和性质。	The simulation results show that the guidance law has higher guidance precision and the high-speed target maneuvering robust, verify the finite time convergence condition and nature.
8415	为了提高无人机对目标的搜索捕获能力和对不确定度较高的区域的回访能力,进而提高多无人机协同搜索效率,提出了一种带信息素回访机制的多无人机协同目标搜索方法。	In order to improve the drones to target search and the ability to capture the high uncertainty region return ability, thus improve the efficiency of multiple unmanned aerial vehicle (uav) cooperative search, with a kind of pheromone is proposed review mechanism for multiple unmanned aerial vehicle (uav) cooperative target search method.
8416	首先,建立了包含目标存在概率地图、不确定地图和数字信息素地图在内的环境感知地图及其更新机理,使得无人机对任务区域中环境和目标信息的感知更加全面和准确,为无人机在线自主地进行搜索决策奠定基础。	First, established the containing object exists probability map, uncertain and digital information map of environment perception map and updating mechanism, makes the unmanned aerial vehicle (uav) perception of task area environment and the target information more comprehensive and accurate, and lay a foundation for unmanned aerial vehicle (uav) independently to search online decision.
8417	其次,考虑到任务子区域的可控回访需求,借鉴信息素"释放-传播-挥发"的特性,设计了基于信息素的网格回访机制,来引导无人机对目标存在可能性较大的区域或者不确定度较高的区域进行回访搜索。	Secondly, considering the task subdomain controllable review requirements, using pheromone release - spread - volatile "features, design a grid based on pheromone review mechanism, to guide the unmanned aerial vehicle (uav) to the target area or the uncertainty of the higher possibility of search area comming back.
8418	最后,设计了基于环境感知地图的协同搜索决策性能指标,在分布式滚动时域优化框架下,建立了多无人机协同搜索决策方法。	Finally, based on environmental perception map of collaborative search decision performance indicators, in a distributed rolling time domain optimization framework, multiple unmanned aerial vehicle (uav) cooperative search decision method is established.
8419	使用蒙特卡罗方法验证了无人机数量、传感器探测靶面半径、传感器性能对搜索效率的影响。	Using the monte carlo method to verify the number, radius of sensors to detect target surface, the influence of the sensor performance on search efficiency.
8420	对比仿真表明,信息素回访机制能够保证较强的遍历能力和回访能力,使得无人机能够尽早搜索到更多的目标,尽快地降低整个搜索区域的不确定度。	The simulation showed that the pheromone review mechanism to guarantee strong ergodic capacity and return ability, enables the uav to search to more goals as soon as possible, as soon as possible to reduce the uncertainty of the search area.
8421	为充分发挥分布式多输入多输出雷达阵元选取的灵活性,提高多目标跟踪精度的同时,尽量降低系统的代价损耗,提出了一种基于多目标位置估计的阵元选取算法。	To give full play to the distributed multiple input multiple output flexibility of radar array element selection, improve the multi-target tracking accuracy at the same time, try to reduce the cost of system loss, this paper proposes a array yuan selection algorithm based on multi-objective location estimation.
8422	首先,以提高目标位置估计精度为优化准则,在给定阵元子集大小和允许最高精度误差的约束下,建立了目标位置估计精度与系统代价损耗折中的阵元选取优化模型。	First of all, in order to improve the target location estimation precision in order to optimize the criterion and arrays in a given subset size constraints, and allow the highest accuracy error of target location estimation precision is established and the system cost loss of eclectic array element selection optimization model.
8423	然后,采用多起点搜索贪婪算法对模型进行求解,得到多目标跟踪的阵元选取结果。	Then, using the starting point to search more greedy algorithm to solve the model, selected arrays of multiple target tracking results are obtained.