Kalman
所属分类：其他
开发工具：C/C++
文件大小：334KB
下载次数：6
上传日期：2019-05-17 20:25:16
上 传 者：
摆摊收幸福
说明： 卡尔曼滤波（Kalman filtering）一种利用线性系统状态方程，通过系统输入输出观测数据，对系统状态进行最优估计的算法。由于观测数据中包括系统中的噪声和干扰的影响，所以最优估计也可看作是滤波过程。
(Kalman filtering (Kalman filtering) is an algorithm that uses the state equation of a linear system to estimate the optimal state of the system through the input and output observation data of the system. Because the observation data include the influence of noise and interference in the system, the optimal estimation can also be regarded as a filtering process.)
文件列表:
Kalman\Kalman\AR_to_SS.m (1107, 2002-05-29)
Kalman\Kalman\convert_to_lagged_form.m (425, 2002-05-29)
Kalman\Kalman\ensure_AR.m (354, 2002-05-29)
Kalman\Kalman\eval_AR_perf.m (1045, 2002-05-29)
Kalman\Kalman\kalman_filter.m (2899, 2002-05-29)
Kalman\Kalman\kalman_forward_backward.m (2392, 2002-11-01)
Kalman\Kalman\kalman_smoother.m (1584, 2002-05-29)
Kalman\Kalman\kalman_update.m (1840, 2002-05-29)
Kalman\Kalman\learning_demo.m (1022, 2002-10-23)
Kalman\Kalman\learn_AR.m (819, 2002-05-29)
Kalman\Kalman\learn_AR_diagonal.m (687, 2002-05-29)
Kalman\Kalman\learn_kalman.m (5498, 2002-05-29)
Kalman\Kalman\sample_lds.m (1797, 2003-01-24)
Kalman\Kalman\smooth_update.m (1199, 2002-05-29)
Kalman\Kalman\SS_to_AR.m (579, 2002-05-29)
Kalman\Kalman\tracking_demo.m (1960, 2003-01-18)
Kalman\kalman_intro.pdf (144971, 2004-09-07)
Kalman\kalman滤波的仿真\kalman.doc (301568, 2005-07-14)
Kalman\kalman滤波的仿真\kalman1.m (1202, 2005-07-14)
Kalman\kalman滤波的仿真\kalman2.m (318, 2005-07-14)
Kalman\Kalman (0, 2004-06-07)
Kalman\kalman滤波的仿真 (0, 2008-06-03)
Kalman (0, 2008-06-03)
Kalman filter toolbox written by Kevin Murphy, 19***.
See http://www.ai.mit.edu/~murphyk/Software/kalman.html for details.
Installation
------------
1. Install KPMtools from http://www.ai.mit.edu/~murphyk/Software/KPMtools.html
3. Assuming you installed all these files in your matlab directory, In Matlab type
addpath matlab/KPMtools
addpath matlab/Kalman
Demos
-----
See tracking_demo.m for a demo of 2D tracking.
See learning_demo.m for a demo of parameter estimation using EM.
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