文件名称:ekf2
- 所属分类：
- 人工智能/神经网络/遗传算法
- 资源属性：
- [Matlab] [源码]
- 上传时间：
- 2016-03-29
- 文件大小：
- 3kb
- 下载次数：
- 0次
- 提 供 者：
- 柳*
- 相关连接：
- 无
- 下载说明：
- 别用迅雷下载，失败请重下，重下不扣分！
介绍说明－－下载内容均来自于网络，请自行研究使用
一种快速Kalman滤波算法实现,。对于某些不能够采取离线计算的滤波过程来说，它可以在保证一定精度的同时极大地提高计算速度和减少计算占用资源- EKF Extended Kalman Filter for nonlinear dynamic systems
[x, P] = ekf(f,x,P,h,z,Q,R) returns state estimate, x and state covariance, P
for nonlinear dynamic system:
x_k+1 = f(x_k)+ w_k
z_k = h(x_k)+ v_k
where w ~ N(0,Q) meaning w is gaussian noise with covariance Q
v ~ N(0,R) meaning v is gaussian noise with covariance R
Inputs: f: function handle for f(x)
x: a priori state estimate
P: a priori estimated state covariance
h: fanction handle for h(x)
z: current measurement
Q: process noise covariance
R: measurement noise covariance
Output: x: a posteriori state estimate
P: a posteriori state covariance
Example:
[x, P] = ekf(f,x,P,h,z,Q,R) returns state estimate, x and state covariance, P
for nonlinear dynamic system:
x_k+1 = f(x_k)+ w_k
z_k = h(x_k)+ v_k
where w ~ N(0,Q) meaning w is gaussian noise with covariance Q
v ~ N(0,R) meaning v is gaussian noise with covariance R
Inputs: f: function handle for f(x)
x: a priori state estimate
P: a priori estimated state covariance
h: fanction handle for h(x)
z: current measurement
Q: process noise covariance
R: measurement noise covariance
Output: x: a posteriori state estimate
P: a posteriori state covariance
Example:
(系统自动生成,下载前可以参看下载内容)
下载文件列表
ekf2\ekf\ekf.m
....\ekf.m
....\ekf
ekf2