粒子滤波方法由于能够灵活地处理非线性非高斯系统而被广泛地应用。
Particle filter is widely used because of its flexibility to deal with the nonlinear non-Gaussian systems.
由于我们实际生活中的系统基本上都是非线性的，因此本文研究的是专门用于非线性非高斯系统跟踪的粒子滤波算法(PF)的基本原理及其具体应用。
Since the real life systems basically are nonlinear, so this paper study the basic principles and specific applications of Particle Filter (PF) specially used for non-linear non-Gaussian tracking.
针对非线性、非高斯系统状态的在线估计问题，本文提出一种新的基于序贯重要性抽样的粒子滤波算法。
In this paper, a new particle filter based on sequential importance sampling (SIS) is proposed for the on-line estimation problem of non-Gauss nonlinear systems.
本文提出了一种在非零均值非平稳高斯激励下获得非线性多自由度系统响应的等效线性化方法。
An equivalent linearization method for obtaining the response of nonlinear multi-degree-of-freedom dynamic systems to nonstationary gaussian excitation with nonzero mean is presented.
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.
为了解决非线性、非高斯系统估计问题，讨论了一种新的滤波方法——高斯粒子滤波算法。
A new Gaussian particle filter (GPF) is discussed to solve estimation problems in nonlinear non-Gaussian systems.
天文导航系统是典型的非线性和噪声非高斯分布的系统。
Autonomous celestial navigation system is a typical nonlinear, non-Gaussian dynamic system.
该文描述了基于贝叶斯推理的目标跟踪算法，可应用于非线性、非高斯系统中。
The paper introduces the target tracking algorithms based on Bayesian inference, which can be applied in the systems of nonlinearity and non-Gaussianity.
该文描述了基于贝叶斯推理的目标跟踪算法，可应用于非线性、非高斯系统中。
The paper introduces the target tracking algorithms based on Bayesian inference, which can be applied in the systems of nonlinearity and non-Gaussianity.
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