ID 原文 译文
6124 最后通过美国国家航空航天局(national aeronautics and space administration,NASA)提供的航空涡轮扇发动机仿真数据集验证了该方法的有效性,其寿命预测性能高于现有几种代表性方法。 Finally by NASA (national aeronautics and space administration, NASA) provide aircraft turbofan engine simulation data sets show the effectiveness of the proposed method, the life prediction performance than the existing several representative methods.
6125 针对空战态势迅速变化对空空导弹攻击区模拟实时性的需求,提出基于背景插值的攻击区在线模拟方法。 According to combat situation rapidly changing simulation of the real time demand of air-to-air missile attack area, put forward based on the background of the interpolation attack area online simulation method.
6126 首先预测下一采样时刻态势,并针对预测态势模拟解算攻击区信息; First to predict the next sampling instant, and predict situational simulation calculating attack area information;
6127 当下一采样时刻到来时,利用攻击区预测值和先前两次攻击区模拟信息及相应的态势记录插值估计攻击区真实值。 Present a sampling time arrival, using the attack area forecast and the previous two attack area and the corresponding simulation information records the interpolation estimates attack area real value.
6128 背景插值方法将攻击区模拟解算放到先前计算周期中,实时性高。 Background will attack area interpolation method simulation calculating in previous calculation cycle, high real-time performance.
6129 理论分析证明了背景插值误差随着采样时间的减小收敛于零。 Theoretical analysis proves that the background with the decrease of the sampling time interpolation error converges to zero.
6130 仿真结果表明,背景插值模拟方法的误差与传统攻击区模拟方法相当,而前者平均可在7.16×10-6 s内给出结果,后者平均计算耗时为0.290s。 Simulation results show that the background error of interpolation method and the traditional attack zone simulation method, and the former in an average of 7.16 x 10-6 s results are given, in which the average computation time is 0.290 s.
6131 针对常规拖曳线列阵目标方位估计中存在的左右舷模糊问题,提出了联合多个时刻机动拖曳线列阵信号模型的稀疏贝叶斯学习空间谱重构估计方法。 For conventional towed line array port/starboard blur problem existing in the of the direction, put forward the combined multiple moment motor towed line array signal model of sparse bayesian learning approach to estimate the spatial spectrum reconstruction.
6132 首先,建立了机动拖曳线列阵的阵元域信号超完备稀疏表示模型; First of all, set up the dynamic arrays towed line array of domain signal complete sparse representation model;
6133 然后,根据稀疏贝叶斯学习原理将目标的空间角度稀疏特性通过信号双层先验假设进行隐性描述; Then, according to the principle of sparse bayesian learning will target the sparse feature space Angle by signal double recessive describe a priori assumptions;