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.
将TVAR模型的信号和反射系数矢量增广为状态矢量后，应用高斯粒子滤波器(GPF)估计TVAR的模型参数，构造了语音增强算法。
When TVAR model signal and reflection coefficients were extended to state vector, Gaussian Particle Filter (GPF) was applied to estimate parameters of TVAR model.
粒子滤波算法由于其在非线性、非高斯模型中所表现出的优良性能，使得其越来越受到人们的重视。
Particle filter algorithm has shown its good performance in non-linear and non-Gaussian models and is paid more and more attention.
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.
本论文以非线性、非高斯噪声环境下的目标跟踪为主要背景，研究弹道导弹目标粒子滤波算法。
In this paper, research on particle filter algorithm for ballistic target tracking is carried on under the main background of nonlinearity, non-Gaussian noise.
为提高被动跟踪性能，提出了一种高斯和粒子滤波方法。
To improve the performance of passive tracking, the Gaussian sum particle filter (GSPF) was proposed.
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.
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.
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.
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