Description: Knowledge of the process noise covariance matrix
is essential for the application of Kalman filtering. However,
it is usually a difficult task to obtain an explicit expression of
for large time varying systems. This paper looks at an adaptive
Kalman filter method for dynamic harmonic state estimation and
harmonic injection tracking.-Knowledge of the process noise covariance matrix is essential for the application of Kalm an filtering. However, it is usually a difficult task to obtain an expli cit for large expression of time varying system s. This paper looks at an adaptive Kalman filter method for dynamic estimation a harmonic state nd harmonic injection tracking. Platform: |
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Author:rifer |
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Description: Carrier-phase synchronization can be approached in a
general manner by estimating the multiplicative distortion (MD) to which
a baseband received signal in an RF or coherent optical transmission
system is subjected. This paper presents a unified modeling and
estimation of the MD in finite-alphabet digital communication systems. A
simple form of MD is the camer phase exp GO) which has to be estimated
and compensated for in a coherent receiver. A more general case with
fading must, however, allow for amplitude as well as phase variations of
the MD.
We assume a state-variable model for the MD and generally obtain a
nonlinear estimation problem with additional randomly-varying system
parameters such as received signal power, frequency offset, and Doppler
spread. An extended Kalman filter is then applied as a near-optimal
solution to the adaptive MD and channel parameter estimation problem.
Examples are given to show the use and some advantages of this scheme. Platform: |
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Author:吴大亨 |
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Description: Introduction to Estimation and the Kalman Filter (KC-1)
Extended Kalman Filter Navigation System Example Platform: |
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Author:刘冬 |
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Description: Kalman filter toolbox written by Kevin Murphy, 1998.
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.
较早版本的kalman滤波matlab源码，适合研读。-Kalman filter toolbox written by Kevin Murphy, 1998.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.html3. Assuming you installed all these files in your matlab directory, In Matlab typeaddpath matlab/KPMtoolsaddpath matlab/KalmanDemos-See tracking_demo.m for a demo of 2D tracking.See learning_demo.m for a demo of parameter estimation using EM. an earlier version of kalman filtering matlab source, suitable for reading. Platform: |
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Author:wuming |
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Description: 卡尔曼滤波的源程序，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。它适合于实时处理和计算机运算。
-Kalman filtering of source code, Kalman filter based on minimum mean square error for estimating the best criteria to search for a recursive estimation algorithm. It is suitable for real-time processing and computing. Platform: |
Size: 1024 |
Author:小雨 |
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Description: 此程序为基于KALMAN滤波的信道估计联合符号检测算法。
mainar1中信道模型为AR1模型。-This procedure is based on the Kalman Filter Channel Estimation Joint symbol detection algorithm. CITIC mainar1 channel model for the AR1 model. Platform: |
Size: 1024 |
Author:左右 |
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Description: 此程序为基于KALMAN滤波的信道估计联合符号检测算法。
modified_jakes为jakes模型子程序。-This procedure is based on the Kalman Filter Channel Estimation Joint symbol detection algorithm. Jakes model for modified_jakes subroutine. Platform: |
Size: 1024 |
Author:左右 |
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Description: 在matlab simulink 中实现kalman滤波器设计，可以生动的演示kalman滤波在运动估计中的预测作用-Matlab simulink in achieving kalman filter design can be a vivid demonstration of kalman filtering in the motion estimation of the prediction Platform: |
Size: 12288 |
Author:冯安祥 |
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Description: 学习扩展卡尔曼滤波气的基本文件，可以随便下载并讨论-This is a tutorial on nonlinear extended Kalman filter (EKF). It uses the standard EKF fomulation to achieve nonlinear state estimation. Inside, it uses the complex step Jacobian to linearize the nonlinear dynamic system. The linearized matrices are then used in the Kalman filter calculation. Platform: |
Size: 55296 |
Author:tongliang |
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Description: 空间谱估计的music仿真程序，以及自适应卡尔曼滤波算法。-Spatial spectrum estimation of the music simulation program, as well as the adaptive Kalman filter algorithm Platform: |
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Author:胡宗恺 |
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Description: 这里有卡尔曼滤波原理及公式陈列，编程实现，外加外界白噪声的影响，旨在加强对卡尔曼滤波的直觉理解与应用。-There Kalman filter theory and formula display, programming, plus white noise of the outside world to enhance intuitive understanding of the Kalman filter and its application. Platform: |
Size: 80896 |
Author:闫骁绢 |
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Description: kalman滤波 针对二阶系统的位置速度估计 适合于初学kalman滤波算法的朋友 代码含有详细解释-kalman filter for the position of second-order system is suitable for beginners speed estimation kalman filter algorithm friend code contains detailed explanations Platform: |
Size: 1024 |
Author:李楠 |
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Description: 通过测量非导通相的反电势波形，运用KALMAN滤波器，估计出转子的位置-By measuring the non-conduction phase of the BEMF waveform, using KALMAN filter, the estimated rotor position Platform: |
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Author:王静 |
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Description: 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。
斯坦利·施密特(Stanley Schmidt)首次实现了卡尔曼滤波器。卡尔曼在NASA埃姆斯研究中心访问时，发现他的方法对于解决阿波罗计划的轨道预测很有用，后来阿波罗飞船的导航电脑使用了这种滤波器。 关于这种滤波器的论文由Swerling (1958), Kalman (1960)与 Kalman and Bucy (1961)发表。
数据滤波是去除噪声还原真实数据的一种数据处理技术, Kalman滤波在测量方差已知的情况下能够从一系列存在测量噪声的数据中，估计动态系统的状态. 由于, 它便于计算机编程实现, 并能够对现场采集的数据进行实时的更新和处理, Kalman滤波是目前应用最为广泛的滤波方法, 在通信, 导航, 制导与控制等多领域得到了较好的应用.(Kalman filtering, also known as linear quadratic estimation (LQE), is an algorithm that uses a series of measurements observed over time, containing statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more accurate than those based on a single measurement alone, by using Bayesian inference and estimating a joint probability distribution over the variables for each timeframe. The filter is named after Rudolf E. , one of the primary developers of its theory.) Platform: |
Size: 3072 |
Author:yxzfrank
|
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Description: 这种滤波器是将过去的测量估计误差合并到新的测量误差中来估计将来的误差。(This filter is to incorporate past measurement estimation errors into new measurement errors to estimate future errors.) Platform: |
Size: 11264 |
Author:huangwh66 |
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