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Description: 扩展Kalman滤波器算法的例程，可以用于对非线性系统的目标状态进行动态估计。例如曲线运动目标的轨迹跟踪。
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Size: 3282 |
Author: lag209@sina.com |
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Description: 工学博士学位论文
目前，扩展卡尔曼滤波是研究初始对准和惯性/GPS组合导航问题的一个主要手段。
但初始对准和惯性/GPS组合导航问题本质上是非线性的，对模型进行线性化的扩展卡
尔曼滤波在一定程度上影响了系统的性能。近年来，直接使用非线性模型的
UKF(Unscented Kalman Filtering, UKF)和粒子滤波，正在逐渐成为研究非线性估计问题
的热点和有效方法。
本文研究了UKF和粒子滤波两种非线性滤波方法，并将其应用于非线性静基座对
准和惯性/GPS组合导航，系统地研究了初始对准和惯性/GPS组合导航中各种非线性项-Engineering PhD thesis Currently, EKF is the initial alignment study and inertial/GPS navigation of a major means. However, initial alignment and inertial/GPS navigation on the nature of the problem is nonlinear. on the model of linear expansion of the Kalman filter certain extent affected the performance of the system. In recent years, direct use of the non-linear model (UKF Unscented Kalman Filtering. UKF) and the particle filter, is gradually becoming nonlinear estimation of the hot and effective method. This paper studies the UKF and particle filter both nonlinear filtering method, will be applied to nonlinear static Base Alignment and inertial/GPS navigation, systematic study of initial alignment and inertial/GPS navigation various nonlinear term
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Author: daniel |
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Description: Unscented Kalman Filter
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Size: 11264 |
Author: LG |
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Description: 一篇介绍无轨迹kalman filter滤波的ppt文章，很清楚的说明了ukf工作的原理和使用方法
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Author: lyh |
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Description: VC++实现的kalman代码，已经检验过了，可以参考。-VC++ Realized kalman code has been tested, and can refer to.
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Size: 76800 |
Author: 王鹏 |
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Description: matlab，非线性卡尔曼滤波（ukf unscented kalman filtering），
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Size: 123904 |
Author: 王海霞 |
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Description: 在对目标进行跟踪时，由于目标的运动方程和观测方程的非线性性，使得滤波时采用传统的卡尔曼滤波器时存在较大误差，不敏卡尔曼滤波器很好的避免了这一点。-Tracking of targets, because the objectives and observation equations of motion of nonlinear equations, and makes filtering when using the traditional Kalman filter when there is a big error, not a very good filter敏卡尔曼avoid this .
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Author: liqiangqiang |
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Description: unscented kalman滤波器程序，相对比较基础，可以结合例子学习，有助于初学者学习-unscented kalman filter procedure, the relative basis of comparison, examples of learning can be combined to help beginners learn
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Size: 9216 |
Author: asdasdasd |
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Description: 介绍了UKF算法及其仿真，希望对大家能够有所帮助。-introduce the unscented kalman filtering
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Size: 2048 |
Author: lanling |
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Description: 比较了 各种nonlinear kalman filter 的优缺点 ukf 和各种ekf-nonlinear KalamanFilters error comparison
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Size: 479232 |
Author: chai |
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Description: The Unscented Kalman Filter (UKF) is a novel development in the field. The idea is to produce several sampling points (Sigma points) around the current state estimate based on its covariance. Then, propagating these points through the nonlinear map to get more accurate estimation of the mean and covariance of the mapping results. In this way, it avoids the need to calculate the Jacobian, hence incurs only the similar computation load as the EKF.
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Size: 2048 |
Author: alazio |
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Description: 研究生期间做的unscented kalman 滤波程序，带测试例子，vc6编译-Graduate students during the unscented kalman filtering process, with the test example, vc6 compiler
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Author: 杨文海 |
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Description: 一种很好的非线性目标跟踪算法，克服了扩展卡尔曼滤波的缺点-A good nonlinear target tracking algorithms, to overcome the shortcomings of the extended Kalman filter
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Size: 2048 |
Author: 汪俊 |
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Description: Matlab交互式多模型UKF和EKF滤波程序（附说明文档）
-Matlab interacting multiple model UKF and EKF filtering procedures (with documentation)
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Size: 309248 |
Author: wangyu |
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Description: 对于扩展卡尔曼滤波在非线性系统中由于线性化过程引入了线性化误差，从而导致滤波器性能下降甚至发散造成滤波发散的情况-For the extended Kalman filter to nonlinear systems of linear process because of the introduction of the linearization error, leading to filter divergence of performance degradation and even filter divergence caused by the situation
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Size: 518144 |
Author: 陈洪 |
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Description: 扩展卡尔曼滤波的MATLAB程序，也许有人能用的到。-Extended kalman filter MATLAB, maybe someone can arrive.
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Author: ranran |
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Description: An implementation of Unscented Kalman Filter for nonlinear state estimation.-Nonlinear state estimation is a challenge problem. The well-known Kalman Filter is only suitable for linear systems. The Extended Kalman Filter (EKF) has become a standarded formulation for nonlinear state estimation. However, it may cause significant error for highly nonlinear systems because of the propagation of uncertainty through the nonlinear system.
The Unscented Kalman Filter (UKF) is a novel development in the field. The idea is to produce several sampling points (Sigma points) around the current state estimate based on its covariance. Then, propagating these points through the nonlinear map to get more accurate estimation of the mean and covariance of the mapping results. In this way, it avoids the need to calculate the Jacobian, hence incurs only the similar computation load as the EKF.
For tutorial purpose, this code implements a simplified version of UKF formulation, where we assume both the process and measurement noises are additive to avoid augment of state and a
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Author: DT丿灬雪狼 |
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Description: matlab code to simulate Unscented kalman filter
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Author: kesang |
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Description: 非线性系统中，应用无迹卡尔曼滤波的例子（英文文章）-Nonlinear system, using unscented Kalman filter example (English article)
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Author: Elvira |
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Description: EKF仅仅利用了非线性函数Taylor展开式的一阶偏导部分（忽略高阶项），常常导致在状态的后验分布的估计上产生较大的误差，影响滤波算法的性能，从而影响整个跟踪系统的性能。最近，在自适应滤波领域又出现了新的算法——无味变换Kalman滤波器（Unscented Kalman Filter-UKF）。UKF的思想不同于EKF滤波，它通过设计少量的σ点，由σ点经由非线性函数的传播，计算出随机向量一、二阶统计特性的传播。因此它比EKF滤波能更好地迫近状态方程的非线性特性，从而比EKF滤波具有更高的估计精度。
-EKF only uses non-linear function of the first-order Taylor expansion of some partial derivatives (ignoring higher order terms), often leading to the posterior distribution of the state estimates to generate large errors affect the performance of filtering algorithms, which affect the whole tracking system performance. Recently, the field of adaptive filtering algorithms and the emergence of new- and tasteless transform Kalman filter (Unscented Kalman Filter-UKF). EKF UKF filter is different from the idea that it points through the design of a small amount of σ by σ point spread through the nonlinear function to calculate the random vector first and second order statistical properties of the transmission. Therefore it is better than the EKF filter nonlinear characteristics equation of state approach, which is more than the EKF filter estimation accuracy.
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Author: zyz |
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