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本实验是利用卡尔曼滤波器实现一维匀加速运动的轨迹跟踪。卡尔曼滤波是一种时域滤波方法，它采用状态空间方法描述系统，算法采用递推机制，数据存储量小，在导航领域有成功的应用。-This experiment is the use of Kalman filter to achieve a one-dimensional constant acceleration of the trajectory tracking. Kalman filter is a time-domain filtering m
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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
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将非平稳时间序列的状态空间建模方法用于陀螺过渡过程的分析.基于平滑先验约束的概念，使用卡尔曼滤波和赤池的AIC方法拟合全局模型，得到陀螺漂移模型的若干数值结果并用于陀螺系统分析.由于观测序列的趋势项、不规则分量可同时建模，因此比分别建模在统计上更加准确有效.-Will be non-stationary time series state space modeling method for the analysis of the transition process gyro. Priori s
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documentation for optimal filtering toolbox for mathematical software
package Matlab. The methods in the toolbox include Kalman filter, extended Kalman filter
and unscented Kalman filter for discrete time state space models. Also included in the
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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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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
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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 al
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kalman filter for state space estimation.
descrat time, with Monte Carlo
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通过建立状态空间模型，使用simulink仿真，其中包括了kalman滤波-Through the establishment of the state space model, using the simulink simulation, including the kalman filter
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卡尔曼滤波的一个显著特点是，用状态空间概念来描述其数学公式。-Kalman filter, a notable feature is the state space to describe the mathematical formulas.
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基于sigma-point的卡尔曼滤波法，用于动态状态空间模型，包括UKF与CDKF，是国外的博士论文，文中给出了各算法的仿真代码-Based on sigma-point Kalman filtering for dynamic state space models, including the UKF and CDKF, the foreign doctoral dissertation, which gives the algorithm of the simulation code
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卡尔曼滤波器的算法C实现
最佳线性滤波理论起源于40年代美国科学家Wiener和前苏联科学家Ｋолмогоров等人的研究工作，后人统称为维纳滤波理论。从理论上说，维纳滤波的最大缺点是必须用到无限过去的数据，不适用于实时处理。为了克服这一缺点，60年代Kalman把状态空间模型引入滤波理论，并导出了一套递推估计算法，后人称之为卡尔曼滤波理论。卡尔曼滤波是以最小均方误差为估计的最佳准则，来寻求一套递推估计的算法，其基本思想是：采用信号与噪声的状态空间模型，利用前一时刻地估计值和现时刻的观测值来
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This the code for kalman fiter, smoothing used in state space model-This is the code for kalman fiter, smoothing used in state space model
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(C-UKF)利用共识协议并将它应用于最优滤波器的非线性状态空间模型-Nonlinear state space model (C-UKF) and applies it to take advantage of the consensus agreement of the optimal filter
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kalman filter main routine can be used to DSGE model and state space model
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Overview
The Simulink model shows an example how the Kalman Filter can be
implemented in Simulink. The model itself is configured with a Gaussian
process connected with a Kalman Filter. To directly use this model, one
only needs to provi
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卡尔曼滤波器学习资料,包括经典拓展卡尔曼滤波器、无迹卡尔曼滤波器等。(good tutorial for Kalman filtering.)
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基于卡尔曼滤波对现有采样数据进行滤波，有效降低观测值的误差。卡尔曼滤波是一种时域方法，它把状态空间的概念引入随机估计理论，用状态方程、观测方程和噪声激励递推估计测量噪声，便于实现实时应用。(The existing sampled data is filtered based on Kalman filter, which can effectively reduce the error of the observed value. Kalman filtering is a time doma
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Functions
kalman_filter
kalman_smoother - implements the RTS equations
learn_kalman - finds maximum likelihood estimates of the parameters using EM
sample_lds - generate random samples
AR_to_SS - convert Auto Regressive model of order k to State
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gauss程序,是KIM那本书里面的附带程序，包含卡尔曼滤波，马尔科夫转移，MCMC等(gauss Kalman filtering mcmc malkov chain)
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