Description: The need for accurate monitoring and analysis of sequential data arises in many scientic, industrial
and nancial problems. Although the Kalman lter is effective in the linear-Gaussian
case, new methods of dealing with sequential data are required with non-standard models.
Recently, there has been renewed interest in simulation-based techniques. The basic idea behind
these techniques is that the current state of knowledge is encapsulated in a representative
sample from the appropriate posterior distribution. As time goes on, the sample evolves and
adapts recursively in accordance with newly acquired data. We give a critical review of recent
developments, by reference to oil well monitoring, ion channel monitoring and tracking
problems, and propose some alternative algorithms that avoid the weaknesses of the current
methods. Platform: |
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Author:阳关 |
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Description: Object-based framework for performing Kalman filtering for discrete time systems or continuous-discrete hybrid systems. Includes code for the classical Kalman filter for linear systems, the extended Kalman filter (EKF), and the more recent unscented Kalman filter (UKF). Both linear and non-linear noise in the system and observation are permitted. Platform: |
Size: 22528 |
Author:mitko |
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Description: 各种kalman滤波器的设计，计算加权加速度,利用matlab GUI实现的串口编程例子，复化三点Gauss-lengend公式求pi，时间序列数据分析中的梅林变换工具，LCMV优化设计阵列处理信号。- Various kalman filter design, Weighted acceleration, Use serial programming examples matlab GUI implementation, Complex of three-point Gauss-lengend the Formula pi, Time series data analysis Mellin transform tool, LCMV optimization design array signal processing. Platform: |
Size: 7168 |
Author:iujcpehe |
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Description: 复化三点Gauss-lengend公式求pi，计算十字叉丝的在不同距离的衍射图像,各种kalman滤波器的设计。- Complex of three-point Gauss-lengend the Formula pi, Calculation crosshairs diffraction image at different distances, Various kalman filter design. Platform: |
Size: 8192 |
Author:张彦 |
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Description: 各种kalman滤波器的设计，是本科毕设的题目，复化三点Gauss-lengend公式求pi。- Various kalman filter design, The title of the commercial is undergraduate course you Complex of three-point Gauss-lengend the Formula pi. Platform: |
Size: 6144 |
Author:puikaipainui |
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Description: 复化三点Gauss-lengend公式求pi，处理信号的时频分析，各种kalman滤波器的设计。- Complex of three-point Gauss-lengend the Formula pi, When processing a signal frequency analysis, Various kalman filter design. Platform: |
Size: 15360 |
Author:唐平 |
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Description: Kalman Filter是一个高效的递归滤波器，它可以实现从一系列的噪声测量中，估 计动态系统的状态。广泛应用于包含Radar、计算机视觉在内的等工程应用领域，在控制理论和控制系统工程中也是一个非常重要的课题。连同线性均方规划，卡尔曼滤波器可以用于解决LQG(Linear-quadratic-Gaussian control)问题。卡尔曼滤波器，线性均方归化及线性均方高斯控制器，是大部分控制领域基础难题的主要解决途径。(Kalman Filter is an efficient recursive filter that can estimate the state of a dynamic system from a range of noise measurements. It is widely used in engineering applications such as Radar and computer vision, and it is also a very important subject in control theory and control system engineering. Along with linear mean square programming, the Calman filter can be used to solve the LQG (Linear-quadratic-Gaussian, control) problem. Calman filter, linear mean square adaptation and linear mean square Gauss controller are the main solutions to the basic problems in most control fields.) Platform: |
Size: 3906560 |
Author:lwzzwl
|
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Description: 5.4.2 Kalman滤波器的设计
这一节将讨论如何使用控制系统工具箱进行Kalman滤波器的设计和仿真。 考虑下面的离散系统：
x［n+1］=Ax［n］+B(u［n］+w［n］) (5.9)
y［n］=Cx［n］ (5.10)
其中, w［n］是在输入端加入的高斯噪声。 状态矩阵参数分别为
A = ［1.1269-0.49400.1129
1.0000 0 0
0 1.0000 0］;
B = ［-0.3832
0.5919
0.5191］;
C = ［1 0 0］;
我们的目标是设计Kalman滤波器, 在给定输入u［n］和带噪输出测量值
yv［n］=Cx［n］+v［n］的情况下估计系统的输出。 其中, v［n］是高斯白噪声。
1） 离散Kalman滤波器
上述问题的稳态Kalman滤波器方程如下：
测量值修正计算(Design of 5.4.2 Kalman filter
This section will discuss how to use the control system toolbox to design and simulate Kalman filters. Consider the following discrete systems:
X [n+1] =Ax [n] +B (u [n] +w [n]) (5.9)
Y [n] =Cx [n] (5.10)
Among them, w [n] is the Gauss noise added at the input end. State matrix parameters are respectively
A = [1.1269-0.49400.1129
1
1];
B = [-0.3832
Zero point five nine one nine
0.5191];
C = [100];
Our goal is to design Kalman filters at given input u [n] and noise output measurements.
The output of the system is estimated in the case of YV [n] =Cx [n] +v [n]. Among them, v [n] is Gauss white noise.
1) discrete Kalman filter
The steady-state Kalman filter equations for the above problems are as follows:
Correction calculation of measurement value) Platform: |
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Author:圆圆圈圈m |
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Search - kalman filter gauss