本文采用的是粒子滤波算法。
在蒙特卡罗方法的基础上，人们提出了粒子滤波算法。
Particle Filter has been proposed based on the Monte Carlo method.
针对侦测系统性能低这一背景，提出一种基于粒子滤波算法的全息检测器。
Since the performance of the detection system is low, a holographic detector base on particle filtering is put forward.
与粒子滤波算法相比，其优点是不需要重采样步骤和不存在粒子退化现象。
Compared with the particle filter (PF), it avoids the resampling step and the particle degeneracy phenomenon.
在估计目标状态时，采用了粒子滤波算法，设计了基于自适应表面模型的观测模型；
When estimating the target state, particle filter is adopted, and the observation model is designed based on the adaptive appearance model.
针对粒子滤波算法建议性函数的选择问题和粒子匮乏现象，提出了改进粒子滤波算法。
Aiming at the choice of proposal function and degeneracy problem in particle filter, an improved algorithm is put forward.
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.
实验结果表明，相比传统的基于颜色直方图的粒子滤波算法，提出的算法具有更好的鲁棒性。
Experimental results indicate that the proposed algorithm has better robustness than the traditional particle filters based on color histogram.
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.
我的主要工作是把粒子滤波算法引人到非线性贝叶斯动态模型中来，对非线性模型进行了模拟。
My main work is applying the particle filter algorithm to random simulate the non-linear Bayesian dynamic models.
用UD分解改进EKF粒子滤波算法，并将其应用于基于星光仰角测量的探测器自主导航方案。
Using UD decomposing to modify EKF Particle filter was imported into the navigation scheme based on the measurement of elevation Angle of star.
目前，实现定位跟踪的算法有很多，如卡尔曼滤波算法、扩展卡尔曼滤波算法、粒子滤波算法等。
At present, there are many algorithms to achieve position tracking, such as the Kalman filter algorithm, extended Kalman filter algorithm, particle filter algorithm and so on.
最后，给出了扩展卡尔曼滤波算法、无迹卡尔曼滤波算法和粒子滤波算法的推导过程和仿真分析。
Finally, the computational procedures and simulation analysis of extended Kalman filtering algorithm, unscented Kalman filtering algorithm and particle filtering algorithm is presented.
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.
粒子滤波算法(PF)中，序列重要性采样引起采样点贫化，进一步经过重采样后造成分集度损失。
In particle filters (PF), sequential importance sampling will result in sample impoverishment and further the loss of diversity after resampling.
针对传统粒子滤波算法中粒子枯竭的缺陷，提出了一种改进的代价参考粒子滤波(CRPF)方法。
Aiming at the shortcoming of particle dry in traditional particle filter, an improved cost reference particle filter (CRPF) was proposed.
本文针对粒子滤波算法提出了两种改进算法：基于粒子位置优化和基于插值优化的粒子滤波跟踪算法。
Two optimal algorithms for particle filter algorithm are proposed in the paper: particle position and the interpolation-based tracking algorithms.
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.
通过粒子滤波技术，研究了如何整合颜色特征、前景信息和积分图运算等技术实现视频目标跟踪的粒子滤波算法。
After studying particle filter technology, a moving object tracking algorithm based on a particle filter was developed, integrating foreground information, color features and an integral image method.
退化现象是应用粒子滤波算法的一个主要障碍，常规的再采样方法虽然可解决退化问题，但容易产生粒子耗尽现象。
Degeneracy phenomenon is a main disadvantage to particle filter application, common re-sampling methods can resolve degeneracy phenomenon, but the sample impoverishment is deduced.
退化现象是应用粒子滤波算法的一个主要障碍，常规的再采样方法虽然可解决退化问题，但容易产生粒子耗尽现象。
The structure of the formula is simple and easy to compute, the linear system method is superior to the former for handling the degeneracy problem.
针对无线传感器网络中的多传感器融合目标跟踪，提出一种混合滤波算法，称为扩展混合粒子滤波算法(EM -PF)。
Aiming at multisensor fusion based target tracking applications in wireless sensor networks, a mixed algorithm is proposed, called extended-mixed particle filter (EM-PF).
因此，本文主要研究基于粒子滤波算法的WLAN室内跟踪技术，并通过对粒子滤波算法的改进来寻找提高室内跟踪精度的算法。
Therefore, this paper studies particle filter algorithm based WLAN indoor tracking technology; further improve indoor positioning accuracy by optimizing particle filter algorithm.
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.
改进后的粒子滤波跟踪算法不但保持了较高的运算效率，而且还较好地提高了跟踪的稳定性。
The improved particle filter tracking algorithm not only keeps the high efficient operation, but also improves stability of target tracking.
提出一种基于粒子滤波的复杂环境下人脸检测与跟踪算法。
A particle filter based face tracking algorithm under complex environment is provide.
提出一种基于粒子滤波的复杂环境下人脸检测与跟踪算法。
A particle filter based face tracking algorithm under complex environment is provide.
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