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Description: 该程序为基于粒子滤波的一种新算法,综合MCMC Bayesian Model Selection即MONTE CARLO马尔克夫链的算法,用来实现目标跟踪,多目标跟踪,及视频目标跟踪及定位等,解决非线性问题的能力比卡尔曼滤波,EKF,UKF好多了,是我珍藏的好东西,现拿出来与大家共享,舍不得孩子套不着狼,希望大家相互支持,共同促进.-the program based on particle filter for a new algorithm, Integrated Bayesian MCMC Model Selection MONTE CARLO that Ma Erkefu chain algorithm, which can be used for target tracking, multi-target tracking, and video tracking and positioning. solve nonlinear problems of capacity than Kalman filtering, EKF, UKF much better, and I treasure the good stuff, now up with the share could not bear children, she sets the wolf, we hope that support each other and work together to promote.
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Size: 14336 |
Author: zhang |
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Description: 采用四元数方法推导出SDINS数学平台角误差方程和速度误差方程，并以此建立非线性初始对准误差模型，应用扩展卡尔曼滤波(EKF)进行精对准，确定初始失调角。-using quaternion approach derived platform SDINS mathematical equations and the error rate of error equation, and the establishment of this initial alignment error nonlinear model, extended Kalman Filter (EKF) for precision alignment, EASY determine the initial angle.
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Size: 110592 |
Author: 李然 |
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Description: UKF滤波方法，改方法比传统的卡尔曼滤波相比，主要是解决非线性问题-UKF filtering method, change methods than the traditional Kalman Filter, the key is to resolve the problem of nonlinear
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Size: 5120 |
Author: 孙希希 |
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Description: To estimate the input-output mapping with inputs x
％ and outputs y generated by the following nonlinear,
％ nonstationary state space model:
％ x(t+1) = 0.5x(t) + [25x(t)]/[(1+x(t))^(2)]
％ + 8cos(1.2t) + process noise
％ y(t) = x(t)^(2) / 20 + 6 squareWave(0.05(t-1)) + 3
％ + time varying measurement noise
％ using a multi-layer perceptron (MLP) and both the EKF and
％ the hybrid importance-samping resampling (SIR) algorithm. -To estimate the input-output mapping with inputs x and outputs y generated by the following nonlinear, nonstationary state space model: x (t+ 1) = 0.5x (t)+ [25x (t )]/[( 1+ x (t)) ^ (2)]+ 8cos (1.2t)+ process noise y (t) = x (t) ^ (2)/20+ 6 squareWave (0.05 (t-1 ))+ 3+ time varying measurement noise using a multi-layer perceptron (MLP) and both the EKF and the hybrid importance-samping resampling (SIR) algorithm.
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Size: 40960 |
Author: Lin |
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Description: EKF uKF的matlab工具箱，适合做非线性滤波的仿真计算。-EKF uKF the matlab toolbox for nonlinear filter to do the simulation.
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Size: 751616 |
Author: 于子予 |
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Description: This paper studies the problem of tracking a ballistic object in
the reentry phase by processing radar measurements. A suitable
(highly nonlinear) model of target motion is developed and the
theoretical Cramer—Rao lower bounds (CRLB) of estimation
error are derived. The estimation performance (error mean and-This paper studies the problem of tracking a ballistic object inthe reentry phase by processing radar measurements. A suitable (highly nonlinear) model of target motion is developed and thetheoretical Cramer-Rao lower bounds (CRLB) of estimationerror are derived. The estimation performance (error mean and
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Size: 1024 |
Author: zhangsheng |
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Description: 迭代扩展卡尔曼滤波，实现非线性滤波的功能，应用在无线传感器网络中-Iterative extended Kalman filter, nonlinear filter function implementation, applications in wireless sensor networks
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Size: 5120 |
Author: ssq |
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Description: 学习扩展卡尔曼滤波气的基本文件，可以随便下载并讨论-This is a tutorial on nonlinear extended Kalman filter (EKF). It uses the standard EKF fomulation to achieve nonlinear state estimation. Inside, it uses the complex step Jacobian to linearize the nonlinear dynamic system. The linearized matrices are then used in the Kalman filter calculation.
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Size: 55296 |
Author: tongliang |
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Description: introduction to nonlinear estimation with EKF and an example in Matlab-introduction to nonlinear estimation with EKF and an example in Matlab
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Size: 219136 |
Author: moatasem momtaz |
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Description: Learning the nonlinear Extended Kalman Filter-Learning the nonlinear Extended Kalman Filter
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Size: 2048 |
Author: tom |
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Description: The state space model is nonlinear and is input to the function along with the current measurement. The function performs the extended Kalman filter update and returns the estimated next state and error covariance
MATLAB 7.6 (R2008a)
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Size: 1024 |
Author: Rafal |
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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: 扩展卡尔曼滤波算法程序，MATLAB编写。相当好用-Extended Kalman filter algorithm program, MATLAB write. Very nice
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Size: 11264 |
Author: 刘海 |
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Description: 一种基于运动模型的扩展卡尔曼滤波（EKF）算法,该方法适用于任何能用状态空间模型表示的非线性系统，精度可以逼近最优估计.-an EKF positioning and tracking algorithm based on kinematic model. This method can apply to any state-space model which is the nonlinear system, and the accuracy can approach to best of all.
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Size: 4096 |
Author: yang |
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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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Size: 130048 |
Author: zyz |
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Description: Extended kalamn filter for tracking manuers linear and nonlinear in motion
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Size: 3072 |
Author: hildass |
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Description: 扩展卡尔曼滤波，非线性EKF的仿真与用法。描述了两个非线性方程x=0.5 x + (25 * x / (1 + x^2)) + 8 * cos(1.2*(t-1)) y = (x^2 / 20)的EKF的用法。简单实用，有备注。对于卡尔曼滤波初学者很有用。-Extended Kalman filter, nonlinear EKF simulation and usage. Describes two nonlinear equations x = 0.5 x+ (25* x/(1+ x ^ 2))+ 8* cos (1.2* (t-1)) y = (x ^ 2/20) of EKF usage. Simple and practical notes. For the Kalman filter useful for beginners.
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Size: 1024 |
Author: Yang Yu |
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Description: 非线性滤波应用，扩展卡尔曼滤波，同时定位与建图(Nonlinear filtering applications, extended Calman filtering, simultaneous localization and mapping)
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Size: 33792 |
Author: feibiaodong
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Description: 一个EKF的简单应用,实现了用EKF算法对三维非线性系统进行仿真。(A simple application of EKF,I used the EKF algorithm to simulate a three-dimensional nonlinear system.)
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Size: 1024 |
Author: Riversnice |
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Description: 扩展卡尔曼滤波可通过将非线性系统在其参考点处作泰勒级数展开，取其一阶线性部分作为该非线性模型的逼近，从而得到非线性系统在当前时刻的线性化描述。(Extended Kalman filter (EKF) can get the linearized description of the nonlinear system at the current time by expanding the nonlinear system with Taylor series at its reference point and taking the first-order linear part as the approximation of the nonlinear model.)
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Size: 9216 |
Author: Hamster_727 |
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