ID	原文	译文
7844	提出基于系统信息量的属性权重确定方法;	Based on system method of determining the attribute weight information;
7845	依据加权阈值粗糙集模型确定上、下近似集,获得初始备件品种决策规则。	Rough set model based on weighted threshold to determine the upper and lower approximation set, for the initial spare parts varieties of decision rules.
7846	结合算例分析探讨模型的有效性,结果表明该方法能够提高备件品种决策精度。	Combined with example analysis to explore the effectiveness of the model, the results show that the method can improve the spare parts varieties decision-making accuracy.
7847	针对传统辐射源威胁评估方法难以直观地给出目标威胁程度以及在实时性、复杂性上的不足,在雷达图法(radar chart method,RCM)的基础上,提出基于改进组合赋权(improved combination weighting,ICW)-RCM的辐射源组合威胁评估算法。	For traditional radiation source threat assessment method is difficult to directly target threat degree is given and the lack of real-time, complex, in entirely (radar chart method, RCM), on the basis of proposed based on improved combination empowerment (improved combination weighting, ICW) - RCM combination of radiation source threat assessment algorithm.
7848	组合威胁评估将辐射源威胁等级粗排序和精细排序相结合,根据雷达指标计算雷达工作模式,对辐射源威胁等级进行粗排序,并降低威胁程度低的辐射源任务优先级;	Combination of threat assessment will sort sort coarse and fine combination of emitter threat level, according to the radar measure work mode, coarse sort of emitter threat level, and reducing low threat level of emitter task priority;
7849	基于ICW-RCM,对相同雷达工作模式的辐射源精细化排序,并结合粗排序结果,得到最终的辐射源威胁评估结果。	ICW - based on RCM, for the same radar working model of radiation source of fine sorting, and combined with coarse sort as a result, get the final result of radiation source threat assessment.
7850	实验仿真与分析表明,该算法具有较好的正确性与有效性,与传统组合赋权雷达图法相比算法复杂度较低、实时性较高、结果更加形象直观。	The experimental simulation and analysis show that the algorithm has good validity and effectiveness, compared with the traditional combination of empowerment entirely algorithm complexity is low, high real-time performance, the results more intuitive image.
7851	针对高频雷达强杂波场景下的机动目标检测,提出了基于希尔伯特黄变换(Hilbert-Huang transform,HHT)的机动目标参数估计算法。	In view of the high frequency LeiDaQiang maneuvering target detection under the clutter scenes, is proposed based on Hilbert Huang transform (Hilbert - Huang transform, HHT) parameter estimation algorithm of maneuvering target.
7852	该算法首先通过复数经验模态分解将回波分解为杂波和目标分量,然后计算目标分量的瞬时频率,获得频率随时间变化的HHT谱,最终利用线性拟合估计目标运动参数。	The algorithm firstly by plural empirical mode decomposition will echo decomposition for clutter and target weight, and then calculate target component of the instantaneous frequency, frequency change over time of HHT spectra, the final linear fitting is used to estimate target motion parameters.
7853	该算法无需抑制杂波,可同时对多目标进行运动参数估计,有助于简化多目标运动补偿和检测流程。	The algorithm need not suppress clutter, but at the same time for multiple target motion parameter estimation and motion compensation method simplifies and testing process.