Description: ：介绍了扩展卡尔曼滤波算法和无迹变换(unscented transformation，UT)算法，并对扩展卡尔曼滤波算法(EKF)和无
迹卡尔曼滤波算法(UKF)进行比较，阐明了UKF优于EKF。在此基础上，提出了一种基于Unscented变换(uT)的高斯和滤
波算法，该算法首先通过合并准则得到适当个数的混合高斯模型，逼近系统中非高斯噪声的概率密度-: Introduction of the extended Kalman filter algorithm and unscented transform (unscented transformation, UT) algorithm, the extended Kalman filter algorithm (EKF) and unscented Kalman filter (UKF) for comparison to clarify the UKF is superior to EKF. On this basis, we propose an approach based on Unscented Transform (uT) and the Gaussian filtering algorithm, which first of all, by merging the appropriate number of criteria to be a mixture of Gaussian model, which was close to the system of the Central African Gaussian noise probability density Platform: |
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Author:lyh |
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Description: 粒子滤波程序，仿真实现自由度机器人对目标的跟踪，使用kalman滤波估计总雅可比矩阵J，噪声为非高斯噪声-Particle filter procedure, simulation robot tracking of targets, the use of kalman filter estimated total Jacobian matrix J, the noise of non-Gaussian noise Platform: |
Size: 6144 |
Author:testit123 |
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Description: The Kalman filter is an efficient recursive filter that estimates the state of a linear dynamic system from a series of noisy measurements. It is used in a wide range of engineering applications from radar to computer vision, and is an important topic in control theory and control systems engineering. Together with the linear-quadratic regulator (LQR), the Kalman filter solves the linear-quadratic-Gaussian control problem (LQG). The Kalman filter, the linear-quadratic regulator and the linear-quadratic-Gaussian controller are solutions to what probably are the most fundamental problems in control theory. Platform: |
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Author:vignesh |
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Description: 粒子滤波器是通过蒙特卡罗模拟来实现递归贝叶斯滤波，它不需要线性、高斯噪声的假设，适用于任何能用状态空间模型表示的非线性系统，比卡尔曼滤波器的适用范围广。这里给出了几个粒子滤波的matlab编程实例。-Particle filters are using Monte Carlo simulations to achieve the recursive Bayesian filtering, it does not require linear, Gaussian noise assumptions, can be used for any state-space model of nonlinear systems .It has a wider scope application than the Kalman filter . Here are a few examples of particle filter matlab programming. Platform: |
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Author:郑玉凤 |
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Description: THIS PROGRAM IS FOR IMPLEMENTATION OF DISCRETE TIME PROCESS UNSCENTED KALMAN FILTER FOR GAUSSIAN AND LINEAR STOCHASTIC DIFFERENCE EQUATION. Platform: |
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Author:Kamdulong |
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Description: 关于kalman滤波的一个比较有代表性的例子，程序中把没有加高斯噪声和加过高斯噪声后进行滤波后的图形用不同颜色的曲线画在了一个图上，便于比较和理解-Kalman filter on a more representative example of the program to add Gaussian noise and processing not been filtered Gaussian noise after the graphic curves of different colors painted on a map, to facilitate comparison and understanding Platform: |
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Author:王会彦 |
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Description: kalman滤波器收到好的结果基于c++和opencv-Tracking of rotating point.
Rotation speed is constant.
Both state and measurements vectors are 1D (a point angle),
Measurement is the real point angle+ gaussian noise.
The real and the estimated points are connected with yellow line segment,
the real and the measured points are connected with red line segment.
(if Kalman filter works correctly,
the yellow segment should be shorter than the red one).
Pressing any key (except ESC) will reset the tracking with a different speed.
Pressing ESC will stop the program. Platform: |
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Author:tbc |
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Description: 本文对于非线性非高斯问题，提出了一种改进扩展卡尔曼滤波（NIEKF）新方法。该方法将迭代滤波理论引入到扩展卡尔曼滤波器方法中，有效地重复利用新的测量信息，还利用Levenberg-Marquardt 方法调整预测协方差阵以保证算法具有全局收敛性。实验结果表明，所提方法具有更高的估计精度，是一种效率较高、性能较好的跟踪方法。-This non-Gaussian for nonlinear problems, an improved extended Kalman filter (NIEKF) the new method. The method of iterative filtering theory is introduced to the extended Kalman filter method, the effective measurement of repeated use of the new information, also using Levenberg-Marquardt method to adjust the covariance matrix of prediction algorithm to ensure global convergence. Experimental results show that the proposed method has higher estimation accuracy, is a high efficiency, good performance tracking methods. Platform: |
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Author:李辉 |
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Description: 本系统中VIS欠缺的SIFT_VC.lib文件。。。
http://www.pudn.com/downloads224/sourcecode/math/detail1055031.html-This is lib file, which is used in Video Intelligent System (VIS) based on the Microsoft Visual Studio 2008 compiler environment and OpenCV 2.0 library. It includes foreground detection, motion object detection, motion object tracking, trajectories generation and analysis modules. It realizes a friendly interface based on dialog, which provides a convenient example for new learners.
keywords: opencv, mixture of gaussian model, sift feature and ransac method, mean shift, particle filter, kalman filter, object detection and tracking, video intelligent system. Platform: |
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Author: |
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Description: 上传一个word档的联邦式扩展卡尔曼粒子滤波算法，大家学习粒子滤波有益，为了使联邦滤波器够有效处理非高斯、非线性系统的状态估计问题，提出将扩展卡尔曼粒子滤波引入联邦滤波结构中，得到一种新的联邦式扩展卡尔曼粒子滤波算法.使用扩展卡尔曼粒子滤波对联邦滤波子系统的多源数据进行处理，从而摆脱了经典卡尔曼滤波的限制，拓宽了联邦滤波器的实际应用范围.将联邦式扩展卡尔曼粒子滤波算法应用于非线性滤波器的一个标准验证模型进行了仿真实验，结果表明该算法是有效性的.-Abstract: A new particle filter(Federated Extend Kalman Particle Filter，EKF-FPF) is proposed to estimate the state of Non-Gaussian and Non-Linear system for federated filter, in which extend kalman particle filer is introduced to federated filter so that the information fusion of subsystem can be solved by the non-gaussian and non-linear filer. By doing so, the federated filter can get rid of the disadvantage of the ordinary kalman filter to extend its application field. The simulation results of the standard testing model demonstrate the feasibility of the proposed algorithm. Platform: |
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Author:宁小磊 |
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Description: 星间精密测距是导航星座实现自主导航的核心技术。针对导航星座中码测量值精度低但无整周模糊度，载
波相位测量值精度高但存在整周模糊度的特点，该文根据贝叶斯递推原理提出了一种衰减记忆高斯和滤波(Fading
Memory Gaussian Sum Filter,FMGSF)的伪距估计方法。该方法用高斯和形式近似表示系统后验概率密度，并根
据卡尔曼滤波原理来更新高斯项的均值和方差，同时引入衰减记忆因子克服由于模型失配导致的滤波结果发散问
题，利用重采样解决由于载波相位测量值不确定导致的算法复杂度增加问题。理论分析和仿真结果表明，该文提出
的方法不仅能够克服周跳对伪距估计的影响，而且可以获得更好的测距精度。
-Precise ranging is the core technology of autonomous navigation.For code measurements yield noisy but
unambiguous pseudorange estimates and the pseudorange obtained with carrier phase measurements are almost
noiseless but are affected by integer ambiguity,a Fading Memory Gaussian Sum Filter(FMGSF)algorithm based
on Bayesian recursive relations is proposed.Posteriori probability density is approximated as a finite Gaussian
mixture,the means and variances of Gaussian terms are updated according to the principle of Kalman filter.
Fading memory factor is imported to avoid the issue of filter divergence due to mismodeling and resampling is
performed to resolve the issue of increasing in computational complexity caused by carrier phase measurement
uncertainty.Theoretical analysis and simulation results show that this algorithm can overcome the effect of cycle
slips to a certain extent and achieve higher range accuracy.
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Author:wangmingxiao |
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Description: 卡尔曼滤波器在目标追踪上的应用，目标真实运动为匀速直线运动，系统噪声为高斯白噪声。-Kalman filter for target tracking application on the target real motion is uniform motion, the system noise is Gaussian white noise. Platform: |
Size: 1024 |
Author:xu huan |
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Description: 粒子滤波算法，通过此程序，可以成功的仿真出粒子滤波。在滤波过程中粒子滤波可以处理任意形式的概率，而不像Kalman滤波只能处理高斯分布的概率问题。他的一大优势也在于此。 -Particle filter, through this program, you can emulate the success of the particle filter. In the process of filtering particle filter can handle any form of probabilities, rather than the Kalman filter can only handle a Gaussian probability distribution. He s a big advantage this. Platform: |
Size: 16384 |
Author:刘龙 |
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Search - kalman filter Gaussian