ID	原文	译文
8344	实测数据实验表明,该检测器相对于IID纹理假设下的最优检测器和自适应归一化匹配滤波器具有一定的性能提升。	Relative to the measured data experiments show that the detector IID texture under the assumption of the optimal detector and adaptive normalized matched filter has a certain performance improvement.
8345	目前使用的很多红外目标跟踪系统在目标背景复杂、目标形体较小、目标受到遮挡等情况下会发生目标丢失现象,针对这一问题,在粒子滤波框架下,提出了一种基于矩阵S1/2范数的红外目标跟踪算法。	Currently use many of the infrared target tracking system in complicated background of target, the target form smaller, target will happen by shade when lost phenomenon, in order to solve this problem, in the framework of particle filter, is put forward based on the matrix S1/2 norm of the infrared target tracking algorithm.
8346	首先,围绕上一帧被跟踪目标的状态对当前帧目标粒子进行采样;	First of all, on a frame around the tracked target state of the current frame target particle samples;
8347	然后,将采样的目标粒子进行筛选,并将筛选后的粒子整体输入到基于矩阵S1/2范数和l1,1范数联合表示的最小化问题模型,并求解其最优解;	Then the goal of sampling particle filtering, and the particles after screening the overall input to S1/2 based on the matrix norm and l1, 1 norm minimization problem model of joint said, and to solve the optimal solution;
8348	最后,根据候选目标粒子在目标字典和背景字典表示下系数的差异确定最优目标粒子,即为当前帧跟踪结果。	Finally, according to the candidate target particles in the dictionary of target and background dictionaries under the said coefficient difference to determine the optimal target particles, is the current frame tracking results.
8349	实验结果表明,相比经典的类似目标跟踪算法,该算法能够对复杂背景、目标形体弱小、目标受到遮挡等多种情况下的红外目标进行有效跟踪,并具有更强的鲁棒性和更好的时效性。	Experimental results show that, compared with the classical similar target tracking algorithm, this algorithm can in complex background, the weak form of target, the target is sheltered that a variety of circumstances such as infrared target tracking effectively, and has stronger robustness and better timeliness.
8350	现代工程系统具有较强的非线性特性,针对这类非线性系统的状态估计问题,提出基于有限单元的贝叶斯原理估计的非线性滤波方法。	Modern engineering system has strong nonlinear characteristics, for this kind of state estimation problem of nonlinear systems, put forward the principle of the bayesian estimates based on the finite element nonlinear filtering method.
8351	采用有限单元法逼近系统状态的先验概率解,即前向Kolmogorov方程的解,通过贝叶斯估计得到状态的后验信息。	Close to the finite element method the prior probability solution of the system state, namely the forward solution of Kolmogorov equation is obtained by bayesian estimation of the state of the posterior information.
8352	将其方法应用到惯性/地形组合导航系统中,仿真结果表明该方法的可行性。	The method is applied to the inertial/terrain in the integrated navigation system, the simulation results show the feasibility of this method.
8353	针对雾霾天气下大气粒子的散射作用导致户外图像质量严重退化和暗通道对天空区域估计失效的问题,提出了一种单幅图像去雾算法。	For fog weather scattering of atmospheric particles induced degeneration of outdoor image quality and dark passage to sky area estimation, solve the problem of a single image to fog algorithm is proposed.