提出了在三维空间中引入径向速度的非线性卡尔曼滤波算法。
Based on three-dimensional space, a new method for the nonlinear Kalman filter using the radial velocity is presented.
在作状态估计时，采用两组非线性卡尔曼滤波切换提高融合精度。
The exchange of two nonlinear Kalman filters was used to improve the fusion accuracy in the state estimation.
目前在信息融合领域广泛使用的融合算法是卡尔曼滤波，它在线性高斯模型下能得到最优估计，但在非线性非高斯模型下则无法应用。
The Kalman Filter is widely applied in the Information Fusion at the present, which can get the optimal estimate in the Linear-Gaussian model, but not applied in the nonlinear and non-Gaussian model.
不敏卡尔曼滤波（UKF）是一种新的非线性滤波的方法，它能减少线性化截断误差对系统定位精度的影响。
Unscented Kalman filter(UKF) is a new nonlinear filtering method which does not linearize the equations thus avoiding the error due to the linearization.
在非线性、非高斯条件下进行动基座传递对准，如果采用卡尔曼滤波会出现误差较大甚至发散的问题。
In moving base transfer alignment under nonlinear and non-Gaussian situation, using Kalman Filtering could cause large error or even divergence.
扩展的卡尔曼滤波定位方法是一个常用的位置跟踪方法，但是在对非线性系统方程进行线性化近似过程中引入了线性化误差。
Extended Kalman Filter is an efficient tool for mobile robot position tracking, but it suffers from linearization errors due to linear approximation of nonlinear system equations.
扩展的卡尔曼滤波方法已经有效地用于非线性模型。
The extended Kalman filtering method has been effectively used in the nonlinear model.
由于状态和观测方程都是非线性的，故采用了扩展的卡尔曼滤波器。
Due to the nonlinearity of the state and measurement equations, the extended Kalman filter is used.
因此，在非线性系统中，基于转换测量值卡尔曼滤波算法的分布融合算法可以重构集中式融合算法。
So it can be concluded that in nonlinear systems distributed fusion algorithm based on converted measurement Kalman filtering can basically reconstruct centralized fusion algorithm.
因此，在非线性系统中，基于转换测量值卡尔曼滤波算法的分布融合算法可以重构集中式融合算法。
So it is concluded that in nonlinear systems distributed fusion algorithm based on converted measurement Kalman filtering can basically reconstruct a centralized fusion algorithm.
本文提出了一种应用推广卡尔曼滤波器来估计非线性系统参数的方法，井获得了较为满意的结果。
In this paper a method of parameter estimation for nonlinear system is proposed by applying the extended Kalman filter and more satisfactory results are obtained.
用非线性车辆模型线性化方法，设计了基于广义卡尔曼滤波器和广义龙贝格观测器的质心侧偏角估计算法。
Through linearizing nonlinear vehicle model, vehicle side-slip Angle estimation algorithms based on generalized Kalman filter and generalized Luenberger observer are formulated.
在确定卫星姿态确定的状态估计法中，经典的扩展卡尔曼滤波(ekf)和新提出的非线性预测滤波(NPF)这两种实时滤波算法各有优缺点。
In state estimation of satellite attitude determination, both traditional extended Kalman filter (EKF) and the proposed nonlinear predictive filter (NPF) have their own merits and defects.
在非线性系统中，常用的跟踪滤波算法是基于扩展的卡尔曼滤波算法的融合算法，但是这种融合算法的跟踪精度并不是很高。
In nonlinear systems, the fusion algorithm based on extended Kalman Filter suffers from the disadvantage that the tracking precision is not satisfied.
无轨迹卡尔曼滤波器(ukf)作为扩展卡尔曼滤波器(ekf)的进化算法在许多非线性估计问题上取得了成功的应用。
Unscented Kalman Filter (UKF), which is an evolutional algorithm of Extended Kalman Filter (EKF), has been successfully applied in many nonlinear estimation problems.
本文应用扩展的卡尔曼滤波估计结构动态参数，文中提出一种简便的减缩变量卡尔曼游波方法，采用等效线性化原理识别结构的非线性参数。
A reduced Kalman filter method for estimating the dynamic parameters of structures is presented in the paper. The results of calculation and test show that this method is effective.
与推广卡尔曼滤波器（EKF）相比，UKF能更好解决量测模型非线性问题，滤波性能更好，而且UKF的计算量与EKF是同阶的。
UKF solves the problem of non-linearity of observation model better, and its performance is superior to that of EKF. The computation complexity of the UKF is the same order as that of the EKF.
对经典的卡尔曼滤波以及针对非线性系统的扩展卡尔曼滤波，不敏卡尔曼滤波算法进行了分析比较。
The state estimations algorithm for Target tracking have been studied and compared such as Kalman filter, Extented Kalman filter and Unscented Kalman filter.
由于扩展卡尔曼滤波必须假定噪声服从高斯分布，若用于复杂非线性系统，其估计精度不甚理想。粒子滤波对噪声类型没有限制，正在成为非线性系统状态估计的有效近似方法。
Because EKF must assume that the noise is subject to Gaussian distribution, the estimate accuracy is not so good if it is used to estimate the state of complicated nonlinear system.
由于扩展卡尔曼滤波必须假定噪声服从高斯分布，若用于复杂非线性系统，其估计精度不甚理想。粒子滤波对噪声类型没有限制，正在成为非线性系统状态估计的有效近似方法。
Because EKF must assume that the noise is subject to Gaussian distribution, the estimate accuracy is not so good if it is used to estimate the state of complicated nonlinear system.
应用推荐