ID	原文	译文
7454	提出根据延迟补偿时间常数和转速进行滞后补偿的方法,并进行了实验验证。实验结果表明,此方法具有良好的补偿效果。	Using lag Angle calculation formula and the method of sine wave frequency sweep, get under low speed steering input instructions signal and response signal time delay compensation is constant;Delay compensation time constant and the speed is put forward according to the lag compensation method, and experimental verification.The experimental results show that this method has a good compensation effect.
7455	当系统模型不能正确描述真实系统时,强跟踪无迹卡尔曼滤波(unscented Kalman filter,UKF)能很好地弥补传统UKF鲁棒性差的不足,保证滤波精度,但需要额外使用无迹变换,极大地增加计算量。	When the system model can not correctly describe the real system, strong tracking no trace Kalman filtering (unscented Kalman filter, UKF) can well make up for the inadequacy of traditional UKF robustness is poor, guarantee the filtering precision, but the need for additional use no trace of transform, greatly increase the amount of calculation.
7456	针对这一问题,利用Taylor展开分析渐消因子在UKF中的机理,建立渐消因子近似引入方法,提出快速强跟踪UKF。	In order to solve this problem, by using Taylor expansion analysis fading factor in the UKF mechanism, establish factor approximation is introduced into the modification method, fast strong tracking UKF is put forward.
7457	基于统计浮点运算次数的方法定性分析计算量,表明快速强跟踪UKF计算量与传统UKF相近。	Based on the statistics of the computation method of qualitative analysis, the number of floating point arithmetic shows strong fast tracking UKF computation with the traditional UKF.
7458	根据滤波收敛性判据,讨论了强跟踪UKF的收敛性。	According to the filter convergence criterion, the convergence of strong tracking UKF is discussed.
7459	仿真实例证明,快速强跟踪UKF滤波精度与强跟踪UKF相差无几,计算量大幅降低。	Simulation examples show that the rapid strong tracking UKF filter accuracy and strong tracking UKF, greatly reduce amount of calculation.
7460	传统高斯混合粒子概率假设密度滤波器(Gaussian mixture particle probability hypothesis density filter,GMP-PHDF)采用先验状态转移概率密度作为重要性密度函数,会出现粒子退化问题。	Traditional Gaussian mixture particle probability hypothesis density filter (Gaussian mixture particle aim-listed probability content density filter, GMP - PHDF) using a priori state transition probability density as the importance density function, particle degradation problems will occur.
7461	而递推更新高斯滤波器依据测量函数梯度渐进式地进行状态更新,可获得更为接近于真实分布的后验估计,但其协方差矩阵易非正定而导致递推中断。	And recursive update gaussian filter based on the measurement function gradient incrementally status updates, can obtain more close to the real distribution of posterior estimate, but its covariance matrix is not positive definite and led to the suspension of recursion.
7462	对此,本文首先分析平方根递推更新高斯滤波器(square-root recursive update Gaussian filter,SR-RUGF)的实现思路,并给出基于容积卡尔曼滤波(cubature Kalman filter,CKF)的SR-RUGF实现步骤。	To this, this paper analysis the square root of the recursive update Gaussian filter (the square - root recursive update Gaussian filter, the SR - RUGF) the implementation of the train of thought, and based on volume of Kalman filter is given (cubature Kalman filter, CKF) SR - RUGF implementation steps.
7463	在此基础上,利用SR-RUGF为GMP-PHDF构建重要性密度函数,进而提出基于平方根递推更新的GMP-PHDF(square-root recursive update GMP-PHDF,SRRU-GMP-PHDF)算法。	On this basis, the use of SR - RUGF as GMP - PHDF construct the importance density function, put forward based on square root recursive update GMP - PHDF (the square - root recursive update GMP - PHDF SRRU - GMP - PHDF) algorithm.