实验结果表明，在非线性系统模型的仿真中，贝叶斯预测滤波框架能够较好的实现对简单物体运动的跟踪和方位的预测。
The results show that in the simulation of non-linear system model, this framework for Bayesian predictive filter can implement the tracking of simple motion and the orientation prediction.
针对带有未知但有界噪声的非线性系统，提出一种椭球集员滤波算法，并将其应用于保证故障检测与隔离。
A set membership filtering algorithm using ellipsoidal sets for nonlinear systems with unknown but bounded noises is proposed and applied to guaranteed fault detection and isolation(FDI).
对于未知的非线性系统，利用误差滤波方法，提出了一种自适应模糊调节器的设计方法。
For unknown nonlinear systems, by using error filtering method, a design method of adaptive fuzzy regulator is proposed.
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.
预测滤波器是一种基于非线性系统模型的滤波方法，它通过使输出一步前向预测误差最小来估计模型误差，具有较高的估计精度。
The Predictive Filter is an estimation method based on nonlinear system model, which determines the optimal model error using a one-step ahead control approach to provide accurate state estimations.
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.
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.
本文提出了一种基于强跟踪滤波器的自适应故障预报方法，能够对一类带时变参数的非线性系统进行故障预报。
This paper presents an adaptive fault prediction method based on strong tracking filter, which can predict faults in a class of nonlinear time varying systems.
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.
文中分析了基于线性滤波器的逆对象建模方法的优缺点，指出了非线性系统用这种方法的不足。
Firstly, analyzed the characteristic of inverse target's modeling method based on linear wave filter, which is quite successful for studying the linear system.
粒子滤波技术是近几年出现的一种非线性滤波技术，它适用于非线性系统以及非高斯噪声模型。
The particle filtering is a nonlinear filtering technology, which is suitable for the nonlinear system and non-Gaussian noise model.
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.
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.
本文提出了一种基于强跟踪滤波器的自适应故障预报方法，能够对一类带时变参数的非线性系统进行故障预报。
Then two better methods that one of correction of model error and the other of nonlinear filter by Strong Tracking Filter were proposed.
本文提出了一种基于强跟踪滤波器的自适应故障预报方法，能够对一类带时变参数的非线性系统进行故障预报。
Then two better methods that one of correction of model error and the other of nonlinear filter by Strong Tracking Filter were proposed.
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