Description: In 1960, R.E. Kalman published his famous paper describing a recursive solution to the discretedata
linear filtering problem [Kalman60]. Since that time, due in large part to advances in digital
computing, the
Kalman filter
has been the subject of extensive research and application,
particularly in the area of autonomous or assisted navigation. A very “friendly” introduction to the
general idea of the Kalman filter can be found in Chapter 1 of [Maybeck79], while a more complete
introductory discussion can be found in [Sorenson70], which also contains some interesting
historical narrative.-In 1960, R. E. Kalman published his famous paper describ ing a recursive solution to the discretedata li near filtering problem [Kalman60]. Since that time, due in large part to advances in digital computi Vi, the Kalman filter has been the subject of extens ive research and application. particularly in the area of autonomous or assis ted navigation. A very "friendly" introductio n to the general idea of the Kalman filter can be f ound in Chapter 1 of [Maybeck79] while a more complete introductory discussion can be found in [Sorenson70] which also contains some interesting historic al narrative. Platform: |
Size: 243985 |
Author:上将 |
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Description: 离散随机线性系统的卡尔曼滤波。
其中13lman.c是卡尔曼滤波函数，4rinv.c是滤波函数中用到的矩阵求逆函数，13lman0.c是主程序。-discrete stochastic linear Kalman filtering system. 13lman.c which is the Kalman filter function, 4rinv.c filtering function is used in the matrix inversion function, is the main program 13lman0.c. Platform: |
Size: 2048 |
Author:通信学生 |
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Description: In 1960, R.E. Kalman published his famous paper describing a recursive solution to the discretedata
linear filtering problem [Kalman60]. Since that time, due in large part to advances in digital
computing, the
Kalman filter
has been the subject of extensive research and application,
particularly in the area of autonomous or assisted navigation. A very “friendly” introduction to the
general idea of the Kalman filter can be found in Chapter 1 of [Maybeck79], while a more complete
introductory discussion can be found in [Sorenson70], which also contains some interesting
historical narrative.-In 1960, R. E. Kalman published his famous paper describ ing a recursive solution to the discretedata li near filtering problem [Kalman60]. Since that time, due in large part to advances in digital computi Vi, the Kalman filter has been the subject of extens ive research and application. particularly in the area of autonomous or assis ted navigation. A very "friendly" introductio n to the general idea of the Kalman filter can be f ound in Chapter 1 of [Maybeck79] while a more complete introductory discussion can be found in [Sorenson70] which also contains some interesting historic al narrative. Platform: |
Size: 243712 |
Author:上将 |
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Description: 根据二维空间内目标作匀速直线运动和匀速圆周运动的特点，在建立目标运动模型和观测模型的基础上采用基于交互多模算法（IMM）的卡尔曼滤波器对机动目标进行跟踪。仿真结果表明，该算法不仅能够对匀速直线运动和匀速圆周运动的目标进行跟踪，而且在运动模型发生变化时，滤波误差也比较小。
关键词：卡尔曼滤波器；目标跟踪；机动；交互多模（IMM）
-two-dimensional space under target for uniform linear motion and uniform circular motion characteristics, the establishment of the target model and observations on the basis of the model based on interactive multi-algorithm (IMM) Kalman Filter right track moving targets. Simulation results show that the method can not only on uniform linear movement and the uniform circular motion of the target tracking, and the campaign model change, the filter error is relatively small. Keywords : Kalman filter; Target tracking; Mobility; Interactive multi-mode (IMM) Platform: |
Size: 60416 |
Author:zhangyun |
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Description: The source is to track a rotating point using Kalman Filter,which is highly efficient and low error occuring.The Kalman Filter only suits for linear environment. Platform: |
Size: 3072 |
Author:黄春瑞 |
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Description: 卡尔曼滤波的vb源程序(现设线性时变系统的离散状态防城和观测方程)-Kalman Filter vb source (now based linear time-varying systems of discrete state Fangcheng and observation equation) Platform: |
Size: 1024 |
Author:爱德 |
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Description: 1960年，卡尔曼发表了他著名的用递归方法解决离散数据线性滤波
问题的论文。从那以后，得益于数字计算技术的进步，卡尔曼滤波器
已成为推广研究和应用的主题，尤其是在自主或协助导航领域。-In 1960, Kalman published his famous recursive solution using discrete data linear filtering problem papers. Since then, figures to benefit from advances in technology, Kalman filter has become the promotion of research and application of the theme, especially in the field of autonomous or assisted navigation. Platform: |
Size: 409600 |
Author:kysuli |
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Description: The Kalman filter is an efficient recursive filter that estimates the state of a linear dynamic system from a series of noisy measurements. It is used in a wide range of engineering applications from radar to computer vision, and is an important topic in control theory and control systems engineering. Together with the linear-quadratic regulator (LQR), the Kalman filter solves the linear-quadratic-Gaussian control problem (LQG). The Kalman filter, the linear-quadratic regulator and the linear-quadratic-Gaussian controller are solutions to what probably are the most fundamental problems in control theory. Platform: |
Size: 306176 |
Author:vignesh |
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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: THIS PROGRAM IS FOR IMPLEMENTATION OF DISCRETE TIME PROCESS UNSCENTED KALMAN FILTER FOR GAUSSIAN AND LINEAR STOCHASTIC DIFFERENCE EQUATION. Platform: |
Size: 2048 |
Author:Kamdulong |
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Description: this matlab code for estimating the static linear system(system function is time variable) with Kalman Filter.
this program is written by matlab 7.0.
Here we want to estimate the below function:
this is matlab code for estimating the static linear system(system function is time variable) with Recursive Least Squre and 2 solutions for better result.
1- using the Covariance Matrix Reseting in a specefic time.
2-using the RLS with Forget Factor
this program is written by matlab 7.0.
Here we want to estimate the below function:
1-u^2+(1+tansig(0.1*(t-375)))*u^3+u^5+3*u^7
finally,there are plots for showing results.-this is matlab code for estimating the static linear system(system function is time variable) with Kalman Filter.
this program is written by matlab 7.0.
Here we want to estimate the below function:
this is matlab code for estimating the static linear system(system function is time variable) with Recursive Least Squre and 2 solutions for better result.
1- using the Covariance Matrix Reseting in a specefic time.
2-using the RLS with Forget Factor
this program is written by matlab 7.0.
Here we want to estimate the below function:
1-u^2+(1+tansig(0.1*(t-375)))*u^3+u^5+3*u^7
finally,there are plots for showing results.
Platform: |
Size: 1024 |
Author:maysam |
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Description: EKF仅仅利用了非线性函数Taylor展开式的一阶偏导部分（忽略高阶项），常常导致在状态的后验分布的估计上产生较大的误差，影响滤波算法的性能，从而影响整个跟踪系统的性能。最近，在自适应滤波领域又出现了新的算法——无味变换Kalman滤波器（Unscented Kalman Filter-UKF）。UKF的思想不同于EKF滤波，它通过设计少量的σ点，由σ点经由非线性函数的传播，计算出随机向量一、二阶统计特性的传播。因此它比EKF滤波能更好地迫近状态方程的非线性特性，从而比EKF滤波具有更高的估计精度。
-EKF only uses non-linear function of the first-order Taylor expansion of some partial derivatives (ignoring higher order terms), often leading to the posterior distribution of the state estimates to generate large errors affect the performance of filtering algorithms, which affect the whole tracking system performance. Recently, the field of adaptive filtering algorithms and the emergence of new- and tasteless transform Kalman filter (Unscented Kalman Filter-UKF). EKF UKF filter is different from the idea that it points through the design of a small amount of σ by σ point spread through the nonlinear function to calculate the random vector first and second order statistical properties of the transmission. Therefore it is better than the EKF filter nonlinear characteristics equation of state approach, which is more than the EKF filter estimation accuracy. Platform: |
Size: 130048 |
Author:zyz |
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Description: In putting together this course pack we decided not to simply include copies of the slides
for the course presentation, but to attempt to put together a small booklet of information
that could stand by itself. The course slides and other useful information, including a new
Java-based Kalman Filter Learning Tool are available at
http://www.cs.unc.edu/~tracker/ref/s2001/kalman/
In addition, we maintain a popular web site dedicated to the Kalman fi lter. This site
contains links to related work, papers, books, and even some software.
http://www.cs.unc.edu/~welch/kalman/
We expect that you (the reader) have a basic mathematical background, suffi cient to
understand explanations involving basic linear algebra, statistics, and random signals.
-In putting together this course pack we decided not to simply include copies of the slides
for the course presentation, but to attempt to put together a small booklet of information
that could stand by itself. The course slides and other useful information, including a new
Java-based Kalman Filter Learning Tool are available at
http://www.cs.unc.edu/~tracker/ref/s2001/kalman/
In addition, we maintain a popular web site dedicated to the Kalman fi lter. This site
contains links to related work, papers, books, and even some software.
http://www.cs.unc.edu/~welch/kalman/
We expect that you (the reader) have a basic mathematical background, suffi cient to
understand explanations involving basic linear algebra, statistics, and random signals.
Platform: |
Size: 537600 |
Author:ele rock |
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Description: 实现了扩展的卡尔曼滤波算法，可以跟踪非线性运动状态的目标-To achieve the extended Kalman filter algorithm can track the status of non-linear motion target Platform: |
Size: 1024 |
Author:李用 |
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Description: 上传一个word档的联邦式扩展卡尔曼粒子滤波算法，大家学习粒子滤波有益，为了使联邦滤波器够有效处理非高斯、非线性系统的状态估计问题，提出将扩展卡尔曼粒子滤波引入联邦滤波结构中，得到一种新的联邦式扩展卡尔曼粒子滤波算法.使用扩展卡尔曼粒子滤波对联邦滤波子系统的多源数据进行处理，从而摆脱了经典卡尔曼滤波的限制，拓宽了联邦滤波器的实际应用范围.将联邦式扩展卡尔曼粒子滤波算法应用于非线性滤波器的一个标准验证模型进行了仿真实验，结果表明该算法是有效性的.-Abstract: A new particle filter(Federated Extend Kalman Particle Filter，EKF-FPF) is proposed to estimate the state of Non-Gaussian and Non-Linear system for federated filter, in which extend kalman particle filer is introduced to federated filter so that the information fusion of subsystem can be solved by the non-gaussian and non-linear filer. By doing so, the federated filter can get rid of the disadvantage of the ordinary kalman filter to extend its application field. The simulation results of the standard testing model demonstrate the feasibility of the proposed algorithm. Platform: |
Size: 265216 |
Author:宁小磊 |
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Description: 结合雷达跟踪的空中目标的实际情况，针对目标运动模型中的线性运动和非线性运动，分别设计了两种模型，并用马尔科夫状态转移矩阵实现IMM算法。最后对交互多模型卡尔曼滤波算法进行了Matlab仿真及结果分析。-Combined with radar tracking air targets, for linear and non-linear movement of target motion model, two models were designed and used a Markov state transition matrix IMM algorithm. Finally, the interacting multiple model Kalman filter algorithm Matlab simulation and results analysis. Platform: |
Size: 25600 |
Author:王延飞 |
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Description: 线性卡尔曼滤波的基本知识介绍，适合初学者学习，通俗易懂，希望对有需要的人有所帮助-Basic knowledge of linear Kalman filter, suitable for beginners to learn, easy to understand, and I hope to help people in need Platform: |
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Author:yuping shao |
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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: 无迹卡尔曼滤波模型，具有详细注释，可修改为自己合适模型(Unscented Kalman filter model, with detailed notes, can be modified to its own appropriate model) Platform: |
Size: 56320 |
Author:wong11264 |
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Search - linear kalman filter