ID	原文	译文
6244	针对舰船装备远海执行战备任务期间备件补给困难的现实,系统研究了基于有限携行备件的装备战备完好性建模方法与计算问题。	Far from the sea on the ship spare parts supply difficult reality during combat mission, carrying system is studied based on the limited spare parts and equipment readiness integrity modeling method and the calculation problem.
6245	首先,探讨了有限备件情形下的单部件战备完好性模型,给出了适合于工程应用的近似计算算法;	First, this paper discusses the limited spare parts of single component readiness reliability model, gives the approximate calculation algorithm is suitable for engineering application;
6246	其次,研究了基于有限备件的系统战备完好性模型,重点给出了备件共用情形下的串联、并联、表决系统的战备完好性算法,并给出系统战备完好性计算流程;	Secondly, based on limited spare parts system of combat readiness integrity model are studied, the key spare parts are given common case of series, parallel, voting system of combat readiness integrity algorithm, and combat readiness system reliability calculation process is given;
6247	最后,通过仿真分析说明,工程实际中常用的稳态可用度计算方法不适用于舰船装备远海执行任务情形下的战备完好性计算,并且分析验证了给出的单部件及备件共用情形下系统的战备完好性近似算法精度较高,可用于舰船装备战备完好性评估。	Finally, through the simulation analysis shows that the calculation method of the steady-state availability is commonly used in engineering practice does not apply to the ship offshore mission of combat readiness reliability calculation, and analysis to verify the effectiveness of the given single parts and spares case system of combat readiness integrity high precision approximation algorithm, can be used to ship combat readiness integrity evaluation.
6248	隐式马尔可夫链(hidden Markov chain,HMC)是传统多目标跟踪的理论基础。	Implicit Markov chain (hidden Markov chain, HMC) is the base of traditional multiple target tracking.
6249	在分析了HMC模型的局限性基础上,介绍了更具普适性的双马尔可夫链(pairwise Markov chain,PMC)模型;	Based on the analysis of the limitations of HMC model, this paper introduces the more general double Markov chain (pairwise Markov chain, PMC) model;
6250	对基于PMC模型的概率假设密度(PMC-probability hypothesis density,PMC-PHD)滤波算法进行了推导;	based on PMC model for the probability hypothesis density (PMC - aim-listed probability content, density, PMC - PHD) filtering algorithm is derived;
6251	并对其高斯混合(Gauss mixture,GM)实现进行了改进,利用椭圆波门给每一个高斯分量建立一个对应的缩减量测集合来对其进行更新。	and the gaussian mixture (Gauss mixture, GM) was improved, using elliptical wave gate to each gaussian component, to establish a set of corresponding reduction measure to upgrade it.
6252	仿真实验证明在杂波密度较大的场景中,PMC-PHD滤波器GM实现的改进在不影响跟踪精度的情况下运行时间缩短为原来的三分之一;	Simulation experiments prove that the clutter density larger scenario, PMC - PHD filter GM to achieve improvements in does not affect the running time of the tracking accuracy for a third of the original;
6253	仿真实验还证明在HMC模型场景下PMC-PHD滤波器针对邻近目标的跟踪性能要优于HMC-PHD滤波器。	Simulation experiments prove that the HMC model scenarios PMC - PHD filter to neighboring target tracking performance is superior to the HMC - PHD filter.