Based on UKF the precision of autonomous navigation was improved obviously.
利用UKF滤波定轨算法，明显提高了自主定轨的精度。
UKF is applied to nonlinear initial alignment of SINS on the stationary base.
将UKF方法应用于静基座对准，研究了非线性静基座对准问题。
However, it can be known that the parameters of UKF are important to the performance of estimate.
但是同时可知，良好的估计效果与ukf算法中各个参数的设置情况有重要的关系。
UKF for two classes of hybrid system are investigated and a specific simplified UKF (SUKF) is raised.
研究了两类混合系统的UK F滤波问题，提出了针对混合系统的简化ukf方法。
A new single-station passive location method combining phase difference and UKF algorithm is presented.
提出了相位差与无迹卡尔曼(ukf)算法相结合的单站无源定位方法。
This paper focuses on the autonomous relative guidance algorithm based on the UKF in the process of proximity.
本文研究了基于UK F滤波器的自主相对导航算法。
After analyzing the model of high dynamic signals, a quasi-open-loop carrier tracking method based on unscented Kalman filter (UKF) is proposed.
在分析高动态载波信号模型的基础上，提出了一种基于无迹卡尔曼滤波(ukf)的准开环载波跟踪方法。
According to the similar computation process of UKF and extended Kalman filter (EKF), the combined Kalman filter based on SSUKF and EKF was designed.
根据UKF和扩展卡尔曼滤波(ekf)计算过程相似的特点，设计了SSUKF和EKF相结合的混合卡尔曼滤波算法。
The experimental results show that the UKF algorithm outperforms the other two in accuracy while its time cost is very much close to the KF algorithm.
实验结果证明，使用UKF算法的跟踪精度优于其他两种算法，时间耗费仅次于KF算法。
Unscented Kalman filter(UKF) is a new nonlinear filtering method which does not linearize the equations thus avoiding the error due to the linearization.
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), which is an evolutional algorithm of Extended Kalman Filter (EKF), has been successfully applied in many nonlinear estimation problems.
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。
The Unsctened Kalman Filter (UKF) for nonlinear system is applied in passive locating by single station, and the general UKF is improved according to the specific application.
将一种适用于非线性系统的UKF应用于单站无源定位，并结合具体应用背景，对通常的UKF作了适当的改进。
The research results of dynamic state estimation algorithm were introduced, which were EKF method, UKF method, predictive Kalman filtering and nonlinear predictive Kalman filtering.
简要分析了几种常见的动态估计方法，它们是EKF方法、UKF方法、预测卡尔曼滤波；
In various noise environments, the comparative studies have been carried out on the fault detection and diagnosis for nonlinear stochastic systems with EKF, UKF and PF, respectively.
在不同噪声环境下，对基于EKF、UKF和PF的非线性随机系统的故障诊断方法进行了比较研究。
For satellite formation flying orbit adjustment situation, which only used radio measurement, a strong tracking unscented Kalman filter (UKF) algorithm was introduced to the simulation.
并针对编队卫星进行轨道机动时仅采用无线电进行测量的工况，采用强跟踪离散卡尔曼滤波(ukf)算法进行仿真计算。
Then the float ambiguity resolution was passed into an unscented Kalman filter as initial state value. UKF estimated the precise ambiguity resolution in real time with the initial value.
再以该近似解和协方差矩阵为初值，由无迹卡尔曼滤波(ukf)实时估计双差整周模糊度的精确解。
The results show that the method has strong tracking capability, robustness and stability. Moreover, a strong tracking UKF algorithm has similar accuracy with that of the normal UKF algorithm.
仿真结果表明该方法具有很强的跟踪能力，并且鲁棒性、稳定性和精度与常规ukf算法相当。
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.
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。
System observability matrix was derived, and the degree of observability was calculated. Relative motion states of non-cooperative space target were estimated through unscented Kalman filter (UKF).
通过计算系统可观测度和采用无迹卡尔曼滤波(ukf)对目标相对运动状态进行估计，研究了观测矢量方向和数量与相对导航精度的关系。
System observability matrix was derived, and the degree of observability was calculated. Relative motion states of non-cooperative space target were estimated through unscented Kalman filter (UKF).
通过计算系统可观测度和采用无迹卡尔曼滤波(ukf)对目标相对运动状态进行估计，研究了观测矢量方向和数量与相对导航精度的关系。
应用推荐