文件名称:kalman滤波的仿真
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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)
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)
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下载文件列表
文件名 | 大小 | 更新时间 |
---|---|---|
kalman滤波的仿真\kalman.doc | 301568 | 2018-01-31 |
kalman滤波的仿真\kalman1.m | 1202 | 2018-01-31 |
kalman滤波的仿真\kalman2.m | 318 | 2018-01-31 |
kalman滤波的仿真 | 0 | 2018-01-31 |
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